LangChain
AICommonly used with
Skills using LangChain (521)
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
phoenix-observability
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
cortex-prompt
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
cortex-recon
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
langchain-ci-integration
Configure CI/CD for LangChain with GitHub Actions, mocked unit tests, gated integration tests, and RAG pipeline validation. Trigger: "langchain CI", "langchain GitHub Actions", "langchain automated tests", "CI langchain", "langchain pipeline testing".
langchain-common-errors
Diagnose and fix common LangChain errors and exceptions. Use when encountering LangChain import errors, auth failures, output parsing issues, agent loops, or version conflicts. Trigger: "langchain error", "langchain exception", "debug langchain", "langchain not working", "langchain troubleshoot".
langchain-core-workflow-a
Build LangChain LCEL chains with prompts, parsers, and composition. Use when creating prompt templates, building RunnableSequence pipelines, parallel/branching chains, or multi-step processing workflows. Trigger: "langchain chains", "langchain prompts", "LCEL workflow", "langchain pipeline", "prompt template", "RunnableSequence".
langchain-core-workflow-b
Build LangChain agents with tool calling for autonomous task execution. Use when creating AI agents, implementing tool/function calling, binding tools to models, or building autonomous multi-step workflows. Trigger: "langchain agents", "langchain tools", "tool calling", "create agent", "function calling", "createToolCallingAgent".
langchain-cost-tuning
Optimize LangChain API costs with token tracking, model tiering, caching, prompt compression, and budget enforcement. Trigger: "langchain cost", "langchain tokens", "reduce langchain cost", "langchain billing", "langchain budget", "token optimization".
langchain-data-handling
Implement LangChain RAG pipelines with document loaders, text splitters, embeddings, and vector stores (Chroma, Pinecone, FAISS). Trigger: "langchain RAG", "langchain documents", "langchain vector store", "langchain embeddings", "document loaders", "text splitters", "retrieval".
langchain-debug-bundle
Collect LangChain debug evidence for troubleshooting and bug reports. Use when preparing GitHub issues, collecting LangSmith traces, or gathering diagnostic info for complex LangChain failures. Trigger: "langchain debug bundle", "langchain diagnostics", "langchain support info", "collect langchain logs", "langchain trace".
langchain-deploy-integration
Deploy LangChain applications to production with LangServe, Docker, and cloud platforms (Cloud Run, AWS Lambda). Trigger: "deploy langchain", "langchain production deploy", "langchain docker", "langchain cloud run", "LangServe".
langchain-enterprise-rbac
Implement role-based access control for LangChain applications with multi-tenant isolation, model access control, and usage quotas. Trigger: "langchain RBAC", "langchain permissions", "langchain access control", "langchain multi-tenant", "enterprise LLM auth".
langchain-hello-world
Create a minimal working LangChain example with LCEL chains. Use when starting a new LangChain integration, testing your setup, or learning LCEL pipe syntax with prompts and output parsers. Trigger: "langchain hello world", "langchain example", "langchain quick start", "simple langchain code", "first langchain app".
langchain-incident-runbook
Incident response procedures for LangChain production issues: provider outages, high error rates, latency spikes, and cost overruns. Trigger: "langchain incident", "langchain outage", "langchain production issue", "langchain emergency", "langchain down", "LLM provider outage".
langchain-install-auth
Install and configure LangChain SDK with provider authentication. Use when setting up a new LangChain project, configuring API keys for OpenAI/Anthropic/Google, or initializing @langchain/core in Node.js or Python. Trigger: "install langchain", "setup langchain", "langchain auth", "configure langchain API key", "langchain credentials".
langchain-local-dev-loop
Configure LangChain local development workflow with testing and mocks. Use when setting up dev environment, creating test fixtures with mocked LLMs, or establishing a rapid iteration workflow for LangChain apps. Trigger: "langchain dev setup", "langchain local development", "langchain testing", "langchain mock", "test langchain chains".
langchain-migration-deep-dive
Migrate to LangChain from raw OpenAI SDK, LlamaIndex, or custom LLM code. Covers codebase assessment, side-by-side validation, RAG migration, agent migration, and feature-flagged gradual rollout. Trigger: "migrate to langchain", "langchain refactor", "legacy LLM migration", "replace openai SDK with langchain", "llamaindex to langchain".
langchain-multi-env-setup
Configure LangChain across dev/staging/production environments with isolated API keys, environment-specific settings, and secrets. Trigger: "langchain environments", "langchain staging", "langchain dev prod", "environment configuration", "langchain env setup".
langchain-observability
Set up comprehensive observability for LangChain applications with LangSmith tracing, OpenTelemetry, Prometheus metrics, and alerts. Trigger: "langchain monitoring", "langchain metrics", "langchain observability", "langchain tracing", "LangSmith", "langchain alerts".
langchain-performance-tuning
Optimize LangChain application performance: latency, throughput, streaming, caching, batch processing, and connection pooling. Trigger: "langchain performance", "langchain optimization", "langchain latency", "langchain slow", "speed up langchain".
langchain-prod-checklist
Production readiness checklist for LangChain applications. Use when preparing for launch, validating deployment readiness, or auditing existing production LangChain systems. Trigger: "langchain production", "langchain prod ready", "deploy langchain", "langchain launch checklist", "go-live langchain".
langchain-rate-limits
Implement LangChain rate limiting, retry strategies, and backoff. Use when handling API rate limits, controlling request throughput, or implementing concurrency-safe batch processing. Trigger: "langchain rate limit", "langchain throttling", "langchain backoff", "langchain retry", "API quota", "429 error".
langchain-reference-architecture
Implement LangChain reference architecture for production systems: layered design, provider abstraction, chain registry, RAG pipelines, and multi-agent orchestration. Trigger: "langchain architecture", "langchain design patterns", "langchain scalable", "langchain enterprise", "LLM architecture".
langchain-sdk-patterns
Apply production-ready LangChain SDK patterns for structured output, fallbacks, batch processing, streaming, and caching. Trigger: "langchain SDK patterns", "langchain best practices", "idiomatic langchain", "langchain architecture", "withStructuredOutput", "withFallbacks", "abatch".
langchain-security-basics
Apply LangChain security best practices for production LLM apps. Use when securing API keys, preventing prompt injection, sandboxing tool execution, or validating LLM outputs. Trigger: "langchain security", "prompt injection", "langchain secrets", "secure langchain", "LLM security", "safe tool execution".
langchain-upgrade-migration
Upgrade LangChain SDK versions safely with import path migration, LCEL conversion from legacy chains, and agent API updates. Trigger: "upgrade langchain", "langchain migration", "langchain breaking changes", "update langchain version", "langchain 0.3", "langchain deprecation".
langchain-webhooks-events
Implement LangChain callback handlers, streaming, webhooks, Server-Sent Events (SSE), and WebSocket integration. Trigger: "langchain callbacks", "langchain webhooks", "langchain events", "langchain streaming", "langchain SSE", "WebSocket LLM".
langfuse-core-workflow-a
Execute Langfuse primary workflow: Tracing LLM calls and spans. Use when implementing LLM tracing, building traced AI features, or adding observability to existing LLM applications. Trigger with phrases like "langfuse tracing", "trace LLM calls", "add langfuse to openai", "langfuse spans", "track llm requests".
langfuse-debug-bundle
Collect Langfuse debug evidence for support tickets and troubleshooting. Use when encountering persistent issues, preparing support tickets, or collecting diagnostic information for Langfuse problems. Trigger with phrases like "langfuse debug", "langfuse support bundle", "collect langfuse logs", "langfuse diagnostic", "langfuse troubleshoot".
langfuse-install-auth
Install and configure Langfuse SDK authentication for LLM observability. Use when setting up a new Langfuse integration, configuring API keys, or initializing Langfuse tracing in your project. Trigger with phrases like "install langfuse", "setup langfuse", "langfuse auth", "configure langfuse API key", "langfuse tracing setup".
lindy-migration-deep-dive
Advanced migration strategies for moving to Lindy AI from other platforms. Use when migrating from Zapier, Make, n8n, custom automations, or consolidating fragmented agent systems. Trigger with phrases like "lindy migration", "migrate to lindy", "zapier to lindy", "switch to lindy", "consolidate automations".
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
conversation-memory
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory
langchain-architecture
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.
langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.
llm-application-dev-langchain-agent
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
oss-hunter
Automatically hunt for high-impact OSS contribution opportunities in trending repositories.
guardrails-ai-setup
Guardrails AI validation framework setup for LLM applications. Implement input/output validation, safety checks, and structured output enforcement.
langgraph-state-graph
LangGraph StateGraph builder with state schema design. Create stateful agent workflows with cycles, conditionals, and persistence.
langsmith-tracing
LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
rag-hybrid-search
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
claude-api
Build apps with the Claude API or Anthropic SDK. TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`/`claude_agent_sdk`, or user asks to use Claude API, Anthropic SDKs, or Agent SDK. DO NOT TRIGGER when: code imports `openai`/other AI SDK, general programming, or ML/data-science tasks.
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.
rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
phoenix-observability
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
chain-of-thought-prompts
Chain-of-thought and step-by-step reasoning prompts for complex problem solving
chroma-integration
Chroma local vector database setup and operations for development and production
constitutional-ai-prompts
Constitutional AI and safety guardrail prompts for aligned LLM behavior
elicit-research-assistant
AI-assisted literature review for question-answering over papers and evidence synthesis
entity-memory-extraction
Entity and fact extraction for user profiling and personalization
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance
langchain-chains
LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns
langchain-memory
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
langchain-react-agent
LangChain ReAct agent implementation with tool binding for reasoning and action loops
langchain-retriever
LangChain retriever implementation with various retrieval strategies for RAG applications
langchain-tools
LangChain tool creation and integration utilities for agent systems
langfuse-integration
LangFuse LLM observability integration for tracing, analytics, and cost tracking
langgraph-checkpoint
LangGraph checkpoint and persistence configuration for stateful workflow management
langgraph-hitl
Human-in-the-loop integration for LangGraph workflows with approval and intervention points
langgraph-routing
Conditional edge routing and state-based transitions for LangGraph workflows
langgraph-subgraph
Subgraph composition and modular workflow design for LangGraph
llm-classifier
LLM-based zero-shot and few-shot classification for flexible intent detection
memory-summarization
Conversation summarization for memory compression and context management
milvus-integration
Milvus distributed vector database configuration for large-scale RAG applications
phoenix-arize-setup
Arize Phoenix observability platform setup for LLM debugging and evaluation
pinecone-integration
Pinecone vector database setup, configuration, and operations for RAG applications
prompt-template-design
Structured prompt template creation with variables, formatting, and version control
qdrant-integration
Qdrant vector database with filtering, payloads, and quantization support
rag-chunking-strategy
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
rag-embedding-generation
Batch embedding generation with caching, rate limiting, and multiple provider support
rag-query-transformation
Query expansion, HyDE, and multi-query generation for improved retrieval
redis-memory-backend
Redis backend for conversation state persistence and caching
weaviate-integration
Weaviate vector database setup with GraphQL queries and hybrid search
zep-memory-integration
Zep memory server integration for long-term conversation memory and user profiling
ai-framework-watch
Weekly competitive-intelligence digest on the AI agent framework space — momentum, releases, breaking changes across a curated watchlist
competitor-launch-radar
Weekly scan of Product Hunt + Hacker News for NEW AI-agent-framework launches outside the 9-framework cohort already tracked by ai-framework-watch
prompt-governance
Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production AI features. Triggers: 'manage prompts in production', 'prompt versioning', 'prompt regression', 'prompt A/B test', 'prompt registry', 'eval pipeline'. NOT for writing or improving individual prompts (use senior-prompt-engineer). NOT for RAG pipeline design (use rag-architect). NOT for LLM cost reduction (use llm-cost-optimizer).
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions.
denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
senior-computer-vision
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
senior-ml-engineer
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
senior-prompt-engineer
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
agent-governance
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
google-cloud-agent-sdk-master
Automatic activation for ALL Google Cloud Agent Development Kit (ADK) and Agent Starter Pack operations - multi-agent systems, containerized deployment, RAG agents, and production orchestration. **TRIGGER PHRASES:** - "adk", "agent development kit", "agent starter pack", "multi-agent", "build agent" - "cloud run agent", "gke deployment", "agent engine", "containerized agent" - "rag agent", "react agent", "agent orchestration", "agent templates" **AUTO-INVOKES FOR:** - Agent creation and scaffolding - Multi-agent system design - Containerized agent deployment - RAG (Retrieval-Augmented Generation) implementation - CI/CD pipeline setup for agents - Agent evaluation and monitoring
langchain-architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
microsoft-foundry-local
Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including troubleshooting, best practices, decision making, configuration, and integrations & coding patterns. Use when using Foundry Local CLI, REST/SDK APIs, OpenAI clients, LangChain/Open WebUI, or Olive model builds, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Tools (use microsoft-foundry-tools), Azure Local (use azure-local).
export-agent
Converts agent definitions between frameworks — exports to Claude Code, OpenAI, CrewAI, Lyzr, and GitHub Models formats, and imports from Claude, Cursor, and CrewAI projects. Use when the user wants to convert an agent, migrate to another framework, export to LangChain/AutoGen/CrewAI, or import from existing automation tools.
denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
senior-computer-vision
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
senior-ml-engineer
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
senior-prompt-engineer
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
adr-drafting
Creates new Architecture Decision Record (ADR) documents for significant architectural changes using a consistent template and repository-aware naming and storage guidance. Use when a user or agent decides on an architectural change, needs to document technical rationale, or wants to add a new ADR to the project history.
aws-cdk
Provides AWS CDK TypeScript patterns for defining, validating, and deploying AWS infrastructure as code. Use when creating CDK apps, stacks, and reusable constructs, modeling serverless or VPC-based architectures, applying IAM and encryption defaults, or testing and reviewing `cdk synth`, `cdk diff`, and `cdk deploy` changes. Triggers include "aws cdk typescript", "create cdk app", "cdk stack", "cdk construct", "cdk deploy", and "cdk test".
aws-cli-beast
Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".
aws-cloudformation-auto-scaling
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
aws-cloudformation-bedrock
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
aws-cloudformation-cloudfront
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
aws-cloudformation-cloudwatch
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
aws-cloudformation-dynamodb
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
aws-cloudformation-ec2
Provides AWS CloudFormation patterns for EC2 instances, Security Groups, IAM roles, and load balancers. Use when creating EC2 instances, SPOT instances, Security Groups, IAM roles for EC2, Application Load Balancers (ALB), Target Groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
aws-cloudformation-ecs
Provides AWS CloudFormation patterns for ECS clusters, task definitions, services, container definitions, auto scaling, blue/green deployments, CodeDeploy integration, ALB integration, service discovery, monitoring, logging, template structure, parameters, outputs, and cross-stack references. Use when creating ECS clusters with CloudFormation, configuring Fargate and EC2 launch types, implementing blue/green deployments, managing auto scaling, integrating with ALB and NLB, and implementing ECS best practices.
aws-cloudformation-elasticache
Provides AWS CloudFormation patterns for ElastiCache Redis or Memcached infrastructure, including subnet groups, parameter groups, security controls, and cross-stack outputs. Use when designing cache tiers, high-availability replication groups, encryption settings, or reusable CloudFormation templates for application caching.
aws-cloudformation-iam
Provides AWS CloudFormation patterns for IAM roles, policies, managed policies, permission boundaries, and trust relationships. Use when modeling least-privilege access, cross-account assumptions, service roles, or reusable IAM stacks that other CloudFormation templates consume.
aws-cloudformation-lambda
Provides AWS CloudFormation patterns for Lambda functions, layers, API Gateway integration, event sources, cold start optimization, monitoring, logging, template validation, and deployment workflows. Use when creating Lambda functions with CloudFormation, configuring event sources, implementing cold start optimization, managing layers, integrating with API Gateway, and deploying Lambda infrastructure.
aws-cloudformation-rds
Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
aws-cloudformation-s3
Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
aws-cloudformation-security
Provides AWS CloudFormation patterns for security infrastructure including KMS encryption, Secrets Manager, IAM security, VPC security, ACM certificates, parameter security, outputs, and secure cross-stack references. Use when implementing security best practices, encrypting data, managing secrets, applying least privilege IAM policies, securing VPC configurations, managing TLS/SSL certificates, and implementing defense in depth strategies.
aws-cloudformation-task-ecs-deploy-gh
Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.
aws-cloudformation-vpc
Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.
aws-drawio-architecture-diagrams
Creates professional AWS architecture diagrams in draw.io XML format (.drawio files) using official AWS Architecture Icons (aws4 library). Use when the user asks for AWS diagrams, VPC layouts, multi-tier architectures, serverless designs, network topology, or draw.io exports involving Lambda, EC2, RDS, or other AWS services.
aws-lambda-java-integration
Provides AWS Lambda integration patterns for Java with cold start optimization. Use when deploying Java functions to AWS Lambda, choosing between Micronaut and Raw Java approaches, optimizing cold starts below 1 second, configuring API Gateway or ALB integration, or implementing serverless Java applications. Triggers include "create lambda java", "deploy java lambda", "micronaut lambda aws", "java lambda cold start", "aws lambda java performance", "java serverless framework".
aws-lambda-php-integration
Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts. Use when deploying PHP/Symfony applications to AWS Lambda, configuring API Gateway integration, implementing serverless PHP applications, or optimizing Lambda performance with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".
aws-lambda-python-integration
Provides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications. Triggers include "create lambda python", "deploy python lambda", "chalice lambda aws", "python lambda cold start", "aws lambda python performance", "python serverless framework".
aws-lambda-typescript-integration
Provides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when creating or deploying TypeScript Lambda functions, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications. Triggers include "create lambda typescript", "deploy typescript lambda", "nestjs lambda aws", "raw typescript lambda", "aws lambda typescript performance".
aws-rds-spring-boot-integration
Provides patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.
aws-sam-bootstrap
Provides AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.
aws-sdk-java-v2-bedrock
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
aws-sdk-java-v2-core
Provides AWS SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK usage.
aws-sdk-java-v2-dynamodb
Provides Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.
aws-sdk-java-v2-kms
Provides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot applications.
aws-sdk-java-v2-lambda
Provides AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.
aws-sdk-java-v2-messaging
Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.
aws-sdk-java-v2-rds
Provides AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
aws-sdk-java-v2-s3
Provides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.
aws-sdk-java-v2-secrets-manager
Provides AWS Secrets Manager patterns for AWS SDK for Java 2.x, including secret retrieval, caching, rotation-aware access, and Spring Boot integration. Use when storing or reading secrets in Java services, replacing hardcoded credentials, or wiring secret-backed configuration into applications.
better-auth
Provides Better Auth integration patterns for NestJS backend and Next.js frontend with Drizzle ORM and PostgreSQL. Use when setting up Better Auth with NestJS backend, integrating Next.js App Router frontend, configuring Drizzle ORM schema, implementing social login (GitHub, Google), adding plugins (2FA, Organization, SSO, Magic Link, Passkey), implementing email/password authentication with session management, or creating protected routes and middleware.
bug-fix-brief
Generates a structured Bug Fix Brief (BFB) to document issue corrections. Includes root cause analysis, repro steps, fix options, and fix checklist. Use when user asks to create a BFB, document a bug fix, or generate a bug correction document.
chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
clean-architecture
Provides implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.
codex
Provides Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex review".
copilot-cli
Provides GitHub Copilot CLI task delegation in non-interactive mode with multi-model support (Claude, GPT, Gemini), permission controls, output sharing, and session resume. Use when users ask to hand work to Copilot, compare models, or run Copilot programmatically from Claude Code.
docs-updater
Provides automated documentation updates by analyzing git changes between the current branch and the last release tag. Performs git diff analysis to identify modifications, then updates README.md, CHANGELOG.md following Keep a Changelog standard, and discovers documentation folders for contextual updates. Use when preparing a release, maintaining documentation sync, or before creating a pull request. Triggers on "update docs", "update changelog", "sync documentation", "update readme", "prepare release documentation".
drawio-logical-diagrams
Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).
drizzle-orm-patterns
Provides comprehensive Drizzle ORM patterns for schema definition, CRUD operations, relations, queries, transactions, and migrations. Proactively use for any Drizzle ORM development including defining database schemas, writing type-safe queries, implementing relations, managing transactions, and setting up migrations with Drizzle Kit. Supports PostgreSQL, MySQL, SQLite, MSSQL, and CockroachDB.
dynamodb-toolbox-patterns
Provides TypeScript patterns for DynamoDB-Toolbox v2 including schema/table/entity modeling, .build() command workflow, query/scan access patterns, batch and transaction operations, and single-table design with computed keys. Use when implementing type-safe DynamoDB access layers with DynamoDB-Toolbox v2 in TypeScript services or serverless applications.
gemini
Provides Gemini CLI delegation workflows for large-context analysis tasks, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for a specific task such as broad codebase analysis or long-document processing. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task".
github-issue-workflow
Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.
graalvm-native-image
Provides expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".
knowledge-graph
Manages persistent Knowledge Graph for specifications. Caches agent discoveries and codebase analysis to remember findings across sessions. Validates task dependencies, stores patterns, components, and APIs to avoid redundant exploration. Use when: you need to cache analysis results, remember findings, reuse previous discoveries, look up what we found, spec-to-tasks needs to persist codebase analysis, task-implementation needs to validate contracts, or any command needs to query existing patterns/components/APIs.
langchain4j-ai-services-patterns
Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.
langchain4j-mcp-server-patterns
Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
langchain4j-rag-implementation-patterns
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
langchain4j-spring-boot-integration
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
langchain4j-testing-strategies
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.
langchain4j-tool-function-calling-patterns
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
langchain4j-vector-stores-configuration
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
learn
Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.
nestjs
Provides comprehensive NestJS framework patterns with Drizzle ORM integration for building scalable server-side applications. Generates REST/GraphQL APIs, implements authentication guards, creates database schemas, and sets up microservices. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM.
nestjs-best-practices
Provides comprehensive NestJS best practices including modular architecture, dependency injection scoping, exception filters, DTO validation with class-validator, and Drizzle ORM integration. Use when designing NestJS modules, implementing providers, creating exception filters, validating DTOs, or integrating Drizzle ORM within NestJS applications.
nestjs-code-review
Provides comprehensive code review capability for NestJS applications, analyzing controllers, services, modules, guards, interceptors, pipes, dependency injection, and database integration patterns. Use when reviewing NestJS code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review NestJS code", "NestJS code review", "check my NestJS controller/service".
nestjs-drizzle-crud-generator
Generates complete CRUD modules for NestJS applications with Drizzle ORM. Use when building server-side features in NestJS that require database operations, including creating new entities with full CRUD endpoints, services with Drizzle queries, Zod-validated DTOs, and unit tests. Triggered by requests like "generate a user module", "create a product CRUD", "add a new entity with endpoints", or when setting up database-backed features in NestJS.
nextjs-app-router
Provides patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).
nextjs-authentication
Provides authentication implementation patterns for Next.js 15+ App Router using Auth.js 5 (NextAuth.js). Use when setting up authentication flows, implementing protected routes, managing sessions in Server Components and Server Actions, configuring OAuth providers, implementing role-based access control, or handling sign-in/sign-out flows in Next.js applications.
nextjs-code-review
Provides comprehensive code review capability for Next.js applications, validates Server Components, Client Components, Server Actions, caching strategies, metadata, API routes, middleware, and performance patterns. Use when reviewing Next.js App Router code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review Next.js code", "Next.js code review", "check my Next.js app".
nextjs-data-fetching
Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing between server and client fetching, setting up caching strategies, or handling loading and error states.
nextjs-deployment
Provides comprehensive patterns for deploying Next.js applications to production. Use when configuring Docker containers, setting up GitHub Actions CI/CD pipelines, managing environment variables, implementing preview deployments, or setting up monitoring and logging for Next.js applications. Covers standalone output, multi-stage Docker builds, health checks, OpenTelemetry instrumentation, and production best practices.
nextjs-performance
Expert Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.
notebooklm
Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
nx-monorepo
Provides comprehensive Nx monorepo management guidance for TypeScript/JavaScript projects. Use when creating Nx workspaces, generating apps/libraries/components, running affected commands, setting up CI/CD, configuring Module Federation, or implementing NestJS backends within Nx
prompt-engineering
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.
qdrant
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
rag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
react-code-review
Provides comprehensive code review capability for React applications, validates component architecture, hooks usage, React 19 patterns, state management, performance optimization, accessibility compliance, and TypeScript integration. Use when reviewing React code changes, before merging pull requests, after implementing new features, or for component architecture validation. Triggers on "review React code", "React code review", "check my React components".
react-patterns
Provides comprehensive React 19 patterns for Server Components, Server Actions, useOptimistic, useActionState, useTransition, concurrent features, Suspense boundaries, and TypeScript integration. Generates executable code patterns, validates security for public endpoints, and optimizes performance with React Compiler or manual memoization. Proactively use when building React 19 applications with Next.js App Router, implementing optimistic UI, or optimizing concurrent rendering.
shadcn-ui
Provides complete shadcn/ui component library patterns including installation, configuration, and implementation of accessible React components. Use when setting up shadcn/ui, installing components, building forms with React Hook Form and Zod, customizing themes with Tailwind CSS, or implementing UI patterns like buttons, dialogs, dropdowns, tables, and complex form layouts.
sonarqube-mcp
Provides SonarQube and SonarCloud integration patterns via the Model Context Protocol (MCP) server. Enables quality gate monitoring, issue discovery and triaging, pre-push code analysis, and rule education directly in the agent workflow. Use when the user wants to check quality gates, search for Sonar issues, analyze code snippets before committing, or understand SonarQube rules. Triggers on "sonarqube", "sonarcloud", "quality gate", "sonar issues", "analyze with sonar", "check sonar", "sonar rule", "pre-push analysis".
spring-ai-mcp-server-patterns
Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.
spring-boot-actuator
Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.
spring-boot-cache
Provides patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.
spring-boot-crud-patterns
Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
spring-boot-dependency-injection
Provides dependency injection patterns for Spring Boot projects, including constructor-first design, optional collaborator handling, bean selection, and wiring validation. Use when creating services and configurations, replacing field injection, or troubleshooting ambiguous or fragile Spring wiring.
spring-boot-event-driven-patterns
Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.
spring-boot-openapi-documentation
Provides patterns to generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.
spring-boot-project-creator
Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.
spring-boot-resilience4j
Provides fault tolerance patterns for Spring Boot 3.x using Resilience4j. Use when implementing circuit breakers, handling service failures, adding retry logic with exponential backoff, configuring rate limiters, or protecting services from cascading failures. Generates circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback implementations. Validates resilience configurations through Actuator endpoints.
spring-boot-rest-api-standards
Provides REST API design standards and best practices for Spring Boot projects. Use when creating or reviewing REST endpoints, DTOs, error handling, pagination, security headers, HATEOAS and architecture patterns.
spring-boot-saga-pattern
Provides distributed transaction patterns using the Saga Pattern for Spring Boot microservices. Use when implementing distributed transactions across services, handling compensating transactions, ensuring eventual consistency, or building choreography or orchestration-based sagas with Kafka, RabbitMQ, or Axon Framework.
spring-boot-security-jwt
Provides JWT authentication and authorization patterns for Spring Boot 3.5.x covering token generation with JJWT, Bearer/cookie authentication, database/OAuth2 integration, and RBAC/permission-based access control using Spring Security 6.x. Use when implementing authentication or authorization in Spring Boot applications.
spring-boot-test-patterns
Provides comprehensive testing patterns for Spring Boot applications covering unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when writing tests, @Test methods, @MockBean mocks, or implementing test suites for Spring Boot applications.
spring-data-jpa
Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
spring-data-neo4j
Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.
tailwind-css-patterns
Provides comprehensive Tailwind CSS utility-first styling patterns including responsive design, layout utilities, flexbox, grid, spacing, typography, colors, and modern CSS best practices. Use when styling React/Vue/Svelte components, building responsive layouts, implementing design systems, or optimizing CSS workflow.
tailwind-design-system
Skill for creating and managing a Design System using Tailwind CSS and shadcn/ui. Use when defining design tokens, setting up theming with CSS variables, building a consistent UI component library, initializing a design system configuration, or wrapping shadcn/ui components into design system primitives.
turborepo-monorepo
Provides comprehensive Turborepo monorepo management guidance for TypeScript/JavaScript projects. Use when creating Turborepo workspaces, configuring turbo.json tasks, setting up Next.js/NestJS apps, managing test pipelines (Vitest/Jest), configuring CI/CD, implementing remote caching, or optimizing build performance in monorepos
typescript-docs
Generates comprehensive TypeScript documentation using JSDoc, TypeDoc, and multi-layered documentation patterns for different audiences. Use when creating API documentation, architectural decision records (ADRs), code examples, and framework-specific patterns for NestJS, Express, React, Angular, and Vue.
typescript-security-review
Provides security review capability for TypeScript/Node.js applications, validates code against XSS, injection, CSRF, JWT/OAuth2 flaws, dependency CVEs, and secrets exposure. Use when performing security audits, before deployment, reviewing authentication/authorization implementations, or ensuring OWASP compliance for Express, NestJS, and Next.js. Triggers on "security review", "check for security issues", "TypeScript security audit".
unit-test-application-events
Provides patterns for unit testing Spring application events. Validates event publishing with ApplicationEventPublisher, tests @EventListener annotation behavior, and verifies async event handling. Use when writing tests for event listeners, mocking application events, or verifying events were published in your Spring Boot services.
unit-test-bean-validation
Provides patterns for unit testing Jakarta Bean Validation (JSR-380), including @Valid, @NotNull, @Min, @Max, @Email constraints with Hibernate Validator. Generates custom validator tests, constraint violation assertions, validation groups, and parameterized validation tests. Validates data integrity logic without Spring context. Use when writing validation tests, bean validation tests, or testing custom constraint validators.
unit-test-boundary-conditions
Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.
unit-test-caching
Provides patterns for unit testing Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict). Generates test code that mocks cache managers, verifies cache hit/miss behavior, tests cache key generation with SpEL expressions, validates eviction strategies, and checks conditional caching scenarios. Triggers: caching tests, test Spring cache, mock cache, Spring Boot caching, cache hit/miss verification, @Cacheable testing.
unit-test-config-properties
Provides patterns for unit testing `@ConfigurationProperties` classes with `@ConfigurationPropertiesTest`. Validates property binding, tests validation constraints, verifies default values, checks type conversions, and mocks property sources for Spring Boot configuration properties. Use when testing application configuration binding, validating YAML or application.properties files, verifying environment-specific settings, or testing nested property structures.
unit-test-controller-layer
Provides patterns for unit testing REST controllers using MockMvc and @WebMvcTest. Generates controller tests that validates request/response mapping, validation, exception handling, and HTTP status codes. Use when testing web layer endpoints in isolation for API endpoint testing, Spring MVC tests, mock HTTP requests, or controller layer unit tests.
unit-test-exception-handler
Provides patterns for unit testing `@ExceptionHandler` and `@ControllerAdvice` in Spring Boot applications. Validates error response formatting, mocks exceptions, verifies HTTP status codes, tests field-level validation errors, and asserts custom error payloads. Use when writing Spring exception handler tests, REST API error tests, or mocking controller advice.
unit-test-json-serialization
Provides patterns for unit testing JSON serialization/deserialization with Jackson and `@JsonTest`. Validates JSON mapping, custom serializers, date formats, and polymorphic types. Use when testing JSON serialization, validating custom serializers, or writing JSON unit tests in Spring Boot applications.
unit-test-mapper-converter
Provides patterns for unit testing mappers, converters, and bean mappings. Validates entity-to-DTO and model transformation logic in isolation. Generates executable mapping tests with MapStruct and custom converter test coverage. Use when writing mapping tests, converter tests, entity mapping tests, or ensuring correct data transformation between DTOs and domain objects.
unit-test-parameterized
Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value analysis.
unit-test-scheduled-async
Provides patterns for unit testing Spring `@Scheduled` and `@Async` methods using JUnit 5, CompletableFuture, Awaitility, and Mockito. Covers mocking task execution and timing, verifying execution counts, testing cron expressions, validating retry behavior, and simulating thread pool behavior. Use when testing background tasks, cron jobs, periodic execution, scheduled tasks, or thread pool behavior.
unit-test-security-authorization
Provides patterns for unit testing Spring Security with `@PreAuthorize`, `@Secured`, `@RolesAllowed`. Validates role-based access control and authorization policies. Use when testing security configurations and access control logic.
unit-test-service-layer
Provides patterns for unit testing service layer with Mockito. Creates isolated tests that mock repository calls, verify method invocations, test exception scenarios, and stub external API responses. Use when testing service behaviors and business logic without database or external services.
unit-test-utility-methods
Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.
unit-test-wiremock-rest-api
Provides patterns for unit testing external REST APIs using WireMock. Stubs API responses, verifies request details, simulates failures (timeouts, 4xx/5xx errors), and validates HTTP client behavior without real network calls. Use when testing service integrations with external APIs or mocking HTTP endpoints.
wiremock-standalone-docker
Provides patterns and configurations for running WireMock as a standalone Docker container. Generates mock HTTP endpoints, creates stub mappings for testing, validates integration scenarios, and simulates error conditions. Use when you need to mock APIs, create a mock server, stub external services, simulate third-party APIs, or fake API responses for integration testing.
wordpress-sage-theme
Provides WordPress theme development patterns using Sage (roots/sage) framework. Use when creating, modifying, or debugging WordPress themes with Sage, including (1): creating new Sage themes from scratch, (2): setting up Blade templates and components, (3): configuring build tools (Vite, Bud), (4): working with WordPress theme templates and hierarchy, (5): implementing ACF fields integration, (6): theme customization and asset management.
zod-validation-utilities
Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
create-readme
Create a README.md file for the project
light-memory-pm
上下文管理、记忆持久化与科研项目管理。当任务涉及长期项目、需要记住项目背景/进展/版本/偏好，或需要把项目拆成阶段任务时使用（常驻）。持续记住研究方向、已定 idea、数据、实验进度、论文/PPT/图表/代码版本、投稿记录、用户偏好、目标期刊。把项目拆成阶段任务并建立任务清单、时间线、里程碑、风险清单与版本记录。
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.
architecting-act
Designs Act and Cast architectures through dynamic questioning, outputting validated CLAUDE.md with mermaid diagrams. Covers resilience patterns (Saga/compensation, long-running threads with DeltaChannel, graceful drain checkpoints, node timeout boundaries) from langgraph v1.2+. Use when starting new Act project, adding cast, planning architecture, extracting sub-cast (10+ nodes), redesigning existing cast, or ask "design architecture", "plan cast", "redesign cast", "create CLAUDE.md".
developing-cast
Implements LangGraph cast components following systematic workflow (state, deps, nodes, conditions, graph). Use when implementing cast, building nodes/agents/tools, need LangGraph patterns (memory, retry, guardrails, vector stores, node timeouts, error handlers, DeltaChannel, graceful shutdown), or ask "implement cast", "build graph", "add node".
developing-deepagent
Implements DeepAgent components using LangChain's deepagents SDK. Use when building deep agents with create_deep_agent, configuring backends/subagents/skills/memory/interpreter, need DeepAgent patterns (sandbox, HITL interrupts, long-term memory, subagent spawning, subagent structured output, QuickJS code interpreter with programmatic tool calling), or ask "implement deep agent", "add subagent", "configure backend", "add interpreter".
streaming-cast
Implements LangGraph v3 event streaming for graphs with subgraphs and agents. Use when adding streaming to runtime/API endpoint, need token streaming, custom stream projections, subagent streaming, or ask "add streaming", "stream tokens", "stream graph".
testing-cast
Guides pytest test writing for LangGraph casts with mocking patterns for LLM/API/Store calls. Use when writing tests, need mock strategies, setting up fixtures, testing nodes/graphs (v3 event streaming, timeouts, error handlers, graceful shutdown), or ask "write tests", "mock LLM", "test coverage".
ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
oss-hunter
Automatically hunt for high-impact OSS contribution opportunities in trending repositories.
pywry-orientation
When to reach for PyWry MCP tools (native webview rendering, Plotly, TradingView, AgGrid, chat) instead of writing Flask/Streamlit/Dash/Electron code. Use at the start of any task involving interactive UI, dashboards, charts, or chat widgets driven from Python.
accessibility
Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries, reduced motion, or cognitive accessibility.
agent-orchestration
Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.
ai-ui-generation
AI-assisted UI generation patterns for json-render, v0.app, Google Stitch, Bolt Cloud, and Cursor workflows. Covers prompt engineering for component and full-stack app generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.
analytics
Queries local analytics across OrchestKit projects for agent usage, skill frequency, hook timing, team activity, session replay, cost estimation, and model delegation trends. Privacy-safe with hashed project IDs. Supports time-range filtering and comparative analysis. Use when reviewing performance, estimating costs, or understanding usage patterns.
animation-motion-design
Animation and motion design patterns using Motion library (formerly Framer Motion) and View Transitions API. Use when implementing component animations, page transitions, micro-interactions, gesture-driven UIs, or ensuring motion accessibility with prefers-reduced-motion.
api-design
API design patterns for REST/GraphQL framework design, versioning strategies, and RFC 9457 error handling. Use when designing API endpoints, choosing versioning schemes, implementing Problem Details errors, or building OpenAPI specifications.
architecture-decision-record
Use this skill when documenting significant architectural decisions. Provides ADR templates following the Nygard format with sections for context, decision, consequences, and alternatives. Use when writing ADRs, recording decisions, or evaluating options.
architecture-patterns
Architecture validation and patterns for clean architecture, backend structure enforcement, project structure validation, test standards, and context-aware sizing. Use when designing system boundaries, enforcing layered architecture, validating project structure, defining test standards, or choosing the right architecture tier for project scope.
ascii-visualizer
ASCII diagram patterns for architecture, workflows, file trees, and data visualizations. Use when creating terminal-rendered diagrams, box-drawing layouts, progress bars, swimlanes, or blast radius visualizations.
assess
Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use when evaluating code, designs, architectures, or comparing alternative approaches.
async-jobs
Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.
bare-eval
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
brainstorm
Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison. Supports open exploration, constrained design, comparison, quick ideation, and iterative optimization modes. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
browser-tools
OrchestKit security wrapper for browser automation. Adds URL blocklisting, rate limiting, robots.txt enforcement, and ethical scraping guardrails on top of the upstream agent-browser skill. Use when automating browser workflows that need safety guardrails.
business-case
Business case analysis with ROI, NPV, IRR, payback period, and TCO calculations for investment decisions. Use when building financial justification, cost-benefit analysis, build-vs-buy comparisons, or sensitivity analysis.
chain-patterns
Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.
checkpoint-resume
Rate-limit-resilient pipeline with checkpoint/resume for long multi-phase sessions. Saves progress to .claude/pipeline-state.json after each phase. Use when starting a complex multi-phase task that risks hitting rate limits, when resuming an interrupted session, or when orchestrating work spanning commits, GitHub issues, and large file changes.
ci-debug
Diagnose a failing CI run against a 10-pattern playbook. Classifies the failure, cites the relevant memory entry, proposes the exact fix command — but NEVER applies without explicit user approval. Use when a specific PR check or GitHub Actions run failed and you want a diagnosis instead of speculation. Don't use for org-wide CI sweeps (that's /status) or for app-level test failures (the playbook is CI-infra-specific).
ci-sentinel
Hourly autonomous classifier for failing PRs across your repos. Runs /ci-debug headless against every open PR with red required checks, posts the verdict as a collapsed PR comment, and appends to a per-repo .sentinel/ledger.jsonl. v1 is propose-don't-apply — NEVER auto-pushes a fix. Use when you're tired of /status sweeps catching the same 10 CI failure patterns over and over.
code-review-playbook
Use this skill when conducting or improving code reviews. Provides structured review processes, conventional comments patterns, language-specific checklists, and feedback templates. Use when reviewing PRs or standardizing review practices.
commit
Creates commits with Conventional Commits format (feat/fix/docs/refactor/test/chore), automatic scope detection, co-author attribution, and pre-commit hook compliance. Validates staged changes, generates descriptive messages focusing on the 'why', and prevents secrets or generated-only files from being committed. Use when committing changes or generating commit messages.
competitive-analysis
Strategic analysis frameworks including Porter's Five Forces (industry attractiveness), SWOT (internal positioning), and competitive landscape mapping with battlecard generation. Produces competitor profiles, feature gap analysis, and positioning recommendations. Use when analyzing market position, evaluating threats, or building sales battlecards.
component-search
Search 21st.dev component registry for production-ready React components. Finds components by natural language description, filters by framework and style system, returns ranked results with install instructions. Use when looking for UI components, finding alternatives to existing components, or sourcing design system building blocks.
configure
Interactive configuration wizard for OrchestKit plugin settings including MCP server enablement, hook permissions, keybindings, and installation presets (Complete/Standard/Lite). Supports preset shortcuts, per-category skill customization, and webhook configuration. Use when customizing plugin behavior or managing settings.
cover
Generate and run comprehensive test suites — unit tests, integration tests with real services (testcontainers/docker-compose), and Playwright E2E tests. Analyzes coverage gaps, spawns parallel test-generator agents per tier, runs tests, and heals failures (max 3 iterations). Use when generating tests for existing code, improving coverage after implementation, or creating a full test suite from scratch. Chains naturally after /ork:implement. Do NOT use for verifying/grading existing tests (use /ork:verify) or running tests without generation (use npm test directly).
create-pr
Creates GitHub pull requests with pre-flight validation, conventional title formatting, and structured summary generation. Runs parallel checks (tests, lint, type-check, security) before opening. Supports feature, bugfix, refactor, and hotfix PR types with milestone assignment via gh CLI. Use when opening PRs or submitting code for review.
database-patterns
Database design and migration patterns for Alembic migrations, schema design (SQL/NoSQL), and database versioning. Use when creating migrations, designing schemas, normalizing data, managing database versions, or handling schema drift.
demo-producer
Universal demo video creator for skills, agents, plugins, tutorials, CLI commands, and code walkthroughs. Generates scripts, storyboards, VHS terminal recordings, and Remotion video compositions with task-tracked production phases. Use when producing video showcases, marketing content, or terminal recordings.
design-context-extract
Extract design DNA from existing app screenshots or live URLs using Google Stitch. Produces color palettes, typography specs, spacing tokens, and component patterns as design-tokens.json or Tailwind config. Use when auditing an existing design, creating a design system from a live app, or ensuring new pages match an established visual identity.
design-import
Imports a Claude Design (claude.ai/design) handoff bundle and scaffolds the proposed components into the project. Accepts a bundle URL or local file, parses and validates the schema, deduplicates components against the existing codebase via component-search, then pipes the survivors through the design-to-code pipeline. Writes provenance metadata so future imports can detect drift between design versions. Use after exporting a handoff bundle from claude.ai/design — this is the entry point that turns a design into code.
design-ship
End-to-end Claude Design handoff to pull request: imports a handoff bundle from claude.ai/design, generates Storybook stories and Playwright tests, runs diff-aware browser verification, and opens a PR with the bundle URL, before/after screenshots, and coverage delta embedded in the body. The one-shot 'design URL in, reviewable PR out' workflow. Use when a designer or PM hands you a Claude Design URL and you want a PR back without intermediate steps.
design-system-tokens
Design token management with W3C Design Token Community Group specification, three-tier token hierarchy (global/alias/component), OKLCH color spaces, Style Dictionary transformation, and dark mode theming. Use when creating design token files, implementing theme systems, managing token versioning, or building design-to-code pipelines.
design-to-code
Mockup-to-component pipeline using Google Stitch, 21st.dev, and Storybook MCP. Accepts screenshots, descriptions, or URLs as input and produces production-ready React components. Checks existing Storybook components before generating, orchestrates design extraction via Stitch MCP, component matching via 21st.dev registry, adaptation to project design tokens, and self-healing verification via run-story-tests. Use when converting visual designs to code, implementing UI from mockups, or building components from screenshots.
dev
One-command dev loop boot. Spins up portless (named HTTPS subdomain), emulate (stateful API mocks), the project's dev server, and an agent-browser session — all using the current git branch as the namespace key. Replaces the 4-terminal manual setup with a single `/ork:dev` invocation. Use when starting a new feature branch, switching worktrees, or returning to a project after a break. Skip silently when prerequisite binaries (portless, emulate, agent-browser) are missing — emits install hints.
devops-deployment
Use when setting up CI/CD pipelines, containerizing applications, deploying to Kubernetes, or writing infrastructure as code. DevOps & Deployment covers GitHub Actions, Docker, Helm, and Terraform patterns.
distributed-systems
Distributed systems patterns for locking, resilience, idempotency, and rate limiting. Use when implementing distributed locks, circuit breakers, retry policies, idempotency keys, token bucket rate limiters, or fault tolerance patterns.
doctor
OrchestKit doctor for health diagnostics across manifest integrity, hook configuration, skill validation, agent frontmatter, MCP server connectivity, CC version compatibility, and permission rules. Reports issues with severity levels and auto-remediation suggestions. Validates component counts, detects orphaned entries, and checks CC version matrix compliance. Use when diagnosing plugin health, troubleshooting configuration issues, or running pre-release checks.
documentation-patterns
Technical documentation patterns for READMEs, ADRs, API docs (OpenAPI 3.1), changelogs, and writing style guides. Use when creating project documentation, writing architecture decisions, documenting APIs, or maintaining changelogs.
domain-driven-design
DDD tactical patterns for complex business modeling including entities, value objects, aggregates, domain services, repositories, specifications, and bounded contexts. Python dataclass implementations with TypeScript alternatives. Use when building rich domain models, enforcing invariants, or separating domain logic from infrastructure.
dream
Nightly memory consolidation — prunes stale entries, merges duplicates, resolves contradictions, rebuilds MEMORY.md index. Use when memory files have accumulated over many sessions and need cleanup. Do NOT use for storing new decisions (use remember) or searching memory (use memory).
emulate-seed
Generate emulate seed configs for stateful API emulation. Wraps Vercel's emulate tool for GitHub, Vercel, Google OAuth, Slack, Apple Auth, Microsoft Entra, AWS (S3/SQS/IAM), Okta, Clerk, Resend, Stripe, and MongoDB Atlas APIs. Not mocks — full state machines where create-a-PR-and-it-appears-in-the-list, send-an-email-and-retrieve-from-local-inbox. Use when setting up test environments, CI pipelines, integration tests, or offline development.
errors
Error pattern analysis and troubleshooting for Claude Code sessions. Categorizes errors (network, auth, model, tool, memory, permission) with known resolution patterns, searches memory for prior occurrences, and suggests recovery steps. Delegates to debug-investigator agent for complex root cause analysis. Use when handling errors, fixing failures, or troubleshooting session issues.
expect
Diff-aware AI browser testing — analyzes git changes, generates targeted test plans, and executes them via agent-browser (Rust daemon + CDP, ARIA-tree-first). Reads git diff to determine what changed, maps changes to affected pages via route map, generates a test plan scoped to the diff, and runs it with pass/fail reporting. Use when testing UI changes, verifying PRs before merge, running regression checks on changed components, or validating that recent code changes don't break the user-facing experience.
explore
Multi-angle codebase exploration spawning 3-5 parallel agents for code structure, data flow, architecture patterns, and health assessment. Generates ASCII visualizations, import graphs, and design pattern detection with cross-session memory storage. Use when exploring a repo, discovering architecture, onboarding to a new codebase, or analyzing design patterns.
feedback
Manages OrchestKit learning system including feedback status, usage pattern tracking, and privacy/analytics consent. Supports pause/resume learning, data export, privacy policy display, and bug reporting. Tracks learned patterns and agent performance metrics. Use when reviewing learned patterns, pausing learning, or managing data consent.
figma-design-handoff
Figma-to-code design handoff patterns including Figma Variables to design tokens pipeline, component spec extraction, Dev Mode inspection, Auto Layout to CSS Flexbox/Grid mapping, and visual regression with Applitools. Use when converting Figma designs to code, documenting component specs, setting up design-dev workflows, or comparing production UI against Figma designs.
fix-issue
Fixes GitHub issues using parallel analysis agents for root cause investigation, code exploration, and regression detection. Reads issue context from gh CLI, searches codebase and memory for related patterns, generates a fix with tests, and links the resolution back to the issue via PR. Includes prevention analysis to avoid recurrence. Use when debugging errors, resolving regressions, fixing bugs, or triaging issues.
github-operations
GitHub CLI operations for issues, PRs, milestones, and Projects v2. Covers gh commands, REST API patterns, and automation scripts. Use when managing GitHub issues, PRs, milestones, or Projects with gh.
i18n-date-patterns
Implements internationalization (i18n) in React applications. Covers user-facing strings, date/time handling, locale-aware formatting, ICU MessageFormat, and RTL support. Use when building multilingual UIs or formatting dates/currency.
verification
Full agent verification suite. Runs security, patterns, quality, and language-specific checks. Use when asked to "verify agent", "verify my agent", "audit agent", or "full verification".
verify-language
Language-specific verification for Python, TypeScript/JavaScript, and Go. Checks type safety, language idioms, and best practices. Use when asked to "verify language", "check types", or for language-specific checks.
verify-patterns
Verify AI agent patterns including loop safety, retry limits, tool consistency, context size, and graph cycle analysis. Use when asked to "verify agent patterns", "check loops", "verify tools", or "check retry limits".
verify-quality
Verify code quality including naming conventions, organization, documentation, and general best practices. Use when asked to "verify quality", "check code quality", or "review code organization".
verify-security
Verify code for security issues including hardcoded secrets, input validation, error exposure, and dependency vulnerabilities. Use when asked to "verify security", "check for secrets", or "scan for vulnerabilities".
deepagents-architecture
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
deepagents-implementation
Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.
langgraph-architecture
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
langgraph-code-review
Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.
langgraph-implementation
Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST
docs-seeker
Fetch up-to-date library and framework documentation into AI context. Use when looking up docs, finding feature-specific references, or discovering documentation sources for any library, framework, or tool.
tavily-search
Web search using Tavily AI-powered search API. Returns AI-synthesized answers with citations and structured search results. Use when the user explicitly requests Tavily search, or when high-quality AI-synthesized answers with citations are needed for research, fact-checking, or comprehensive information gathering. Requires TAVILY_API_KEY.
langsmith-fetch
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no p
planning-with-files
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear.
rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
ui-design
Comprehensive UI design system: 230+ font pairings, 48 themes, 65 design systems, 23 design languages, 30 UX laws, 14 color systems, Swiss grid, Gestalt principles, Pencil.dev workflow. Inherits ui-ux-pro-max (99 UX rules) + impeccable-frontend-design (anti-AI-slop). Triggers on any design, UI, layout, typography, color, theme, or styling task.
add-temporal-context-memory-to-agent-workflows-with-zep
Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.
denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
llm-application-dev-langchain-agent
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
scaffolding-openai-agents
Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration. Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists. Covers Agent class, Runner patterns, function tools, guardrails, and streaming responses. NOT when using raw OpenAI API without SDK or other agent frameworks like LangChain.
senior-computer-vision
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
senior-ml-engineer
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
senior-prompt-engineer
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
autonomous_building
Autonomous PyWry application building using LLM sampling, elicitation, and progress reporting.
chat_agent
How an agent operates inside a PyWry chat widget — reading user messages, attachments, @-context, tool-call result cards, edit/resend, settings changes.
events
The PyWry event system — namespaced events, request/response round-trips, widget IDs, component IDs, and how tool results flow back to the agent.
tvchart
Drive a live TradingView Lightweight Charts widget end-to-end via PyWry MCP tools — symbol, interval, indicators, markers, price lines, layouts, state.
multi-agent-architect
Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows.
multi-agent-orchestration
Coordinate multiple AI agents for complex tasks — decomposition, delegation, and synthesis
agentic-product-architect
Master skill for building production-grade agentic products — software systems where part of the process is dynamically directed by LLMs within deterministic architecture with explicit trust boundaries. Use this skill whenever the user mentions building an agent, agentic product, agentic workflow, AI agent, multi-agent system, agent loop, agent harness, or asks how to design, architect, ship, or harden any system with LLM-driven decision-making. Also use when they reference frameworks like LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Pydantic AI, AutoGen, or when they want to add tools, memory, evals, or human-in-the-loop to an LLM system. This is the entry point — it routes to specialized sub-skills for architecture, context engineering, harness, tools/MCP, memory, durable execution, evals, framework choice, production readiness, and antipattern review.
antipatterns-review
Review existing agentic code, designs, or plans through the lens of the 12 canonical antipatterns. Diagnose what's likely to fail in production. Use whenever the user asks you to review their agent code, asks "what's wrong with this design," is debugging mysterious failures, or wants a second opinion on an architecture. Also use proactively when you notice any of the 12 antipatterns in a conversation, even if the user didn't ask for review.
context-engineering
Engineer what goes into the LLM context window — system prompts, retrieved docs, tool schemas, conversation history, memory, examples. Apply the four operations write/select/compress/isolate to manage context as a finite resource. Enforce the 40% rule on context utilization. Use whenever the user is designing system prompts, debugging quality degradation in long conversations, hitting context limits, managing per-step retrieval, dealing with sub-agent context isolation, or asking about "context engineering" / "prompt engineering" / CLAUDE.md / AGENTS.md / instruction files.
framework-selection
Choose the right agentic framework — LangGraph, OpenAI Agents SDK, Claude Agent SDK, CrewAI, Pydantic AI, AutoGen/AG2, LlamaIndex Workflows, Semantic Kernel, Mastra, DSPy, mcp-agent — based on the team's dominant constraint, not hype. Use whenever the user asks "which framework should I use," compares any two of these, hits limits with their current framework, or is starting a new project and needs to pick the stack.
harness-engineering
Design the harness — the 7-layer scaffolding around the LLM loop that makes agents reliable. Covers the agent loop itself (gather/act/verify), context management, durable execution, guardrails, human-in-the-loop, evals, and observability. In production agents, the harness is 98% of the code. Use whenever the user is structuring code around an agent loop, asks "how do I make this reliable / production-ready," is implementing verification, retry logic, sub-agent delegation, permission systems, approval gates, or wants to understand what makes Claude Code / Codex / Devin work beyond the model.
route-tester
Framework-agnostic HTTP API route testing patterns, authentication strategies, and integration testing best practices. Supports REST APIs with JWT cookie authentication and other common auth patterns.
signet
Cryptographic signing for every tool call with Ed25519 audit trail
nextjs
Next.js 15+ App Router development patterns
shadcn
shadcn/ui component library patterns
tailwindcss
Tailwind CSS v4 utility-first styling patterns
langgraph-docs
Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.
datarobot-agent-assist
Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.md, dr-assist, datarobot-agent-assist, dress rehearsal, or the DataRobot agent template; wants to scaffold a LangGraph, CrewAI, LlamaIndex, NAT, or Base agent targeting DataRobot; wants to add an MCP server, backend API, or React frontend to a DataRobot agent application; or uses the DataRobot CLI (dr) to build or deploy an agentic custom application. Covers the full workflow: agent design, agent_spec.md authoring, dress-rehearsal simulation via the DataRobot LLM Gateway, template-based coding, and deployment.
datarobot-external-agent-monitoring
Instrument any external AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when adding observability to external agents or sending telemetry data to DataRobot.
ghostswap-partners-api
Integrate the GhostSwap Partners API to add no-KYC crypto swaps (1,600+ coins, 0–4% partner fee on top of the swap, USDT payouts) into wallets, dApps, exchanges, payment flows, or affiliate sites. Use whenever a developer asks to "add crypto swap", "embed a swap widget", "let users exchange tokens", "convert crypto to crypto", "earn commission on referred swaps", or wants a server-side REST integration that handles quote → create → status-poll without holding signing keys. Covers Bearer auth, live float and fixed-rate quotes, idempotent swap creation, status lifecycle polling, address validation, error envelope handling, rate limits, and the partner application + payout flow at partners.ghostswap.io.
pr-contribution-excellence
Patterns for excellent open-source PR contributions, distilled from analyzing real PRs across repositories
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no p
search-conference-talks
Use when the task needs content from ML/AI conference presentations, workshop talks, or keynotes — especially recent conferences whose papers may not be fully indexed yet.
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.
ultrathink
UltraThink Workflow OS — 4-layer skill mesh with persistent memory and privacy hooks for complex engineering tasks. Routes prompts through intent detection to activate the right domain skills automatically.
azure-ai-foundry-local
Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including troubleshooting, best practices, decision making, configuration, and integrations & coding patterns. Use when using Foundry Local CLI, chat/transcription APIs, tools, OpenAI/LangChain clients, or upgrading legacy SDKs, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Tools (use microsoft-foundry-tools), Azure Local (use azure-local).
security-guide
OpenClaw 安全部署指南 / Security deployment guide — help users secure their OpenClaw installation
apple-reminders
Manage Apple Reminders via remindctl CLI: list, add, edit, complete, delete. Supports lists, date filters, and JSON output. Syncs to iOS devices.
github
GitHub operations: list/create issues, PRs, check CI, manage repos. Requires GH_TOKEN.
notion
Manage Notion workspace: create pages, query databases, add content blocks. Requires NOTION_API_KEY.
peekaboo
macOS UI automation: capture screenshots, click elements, type text, manage apps/windows/menus. Full desktop control via peekaboo CLI.
spotify
Control Spotify playback: play, pause, skip, search tracks/albums/playlists, manage devices and queue via spogo CLI.
Send and read WhatsApp messages via the wacli CLI. Search chats, view history, send text and files to contacts or groups.
express
Express.js framework patterns including routing, middleware, request/response handling, and Express-specific APIs. Use when working with Express routes, middleware, or Express applications.
mui
Material-UI v7 component library patterns including sx prop styling, theme integration, responsive design, and MUI-specific hooks. Use when working with MUI components, styling with sx prop, theme customization, or MUI utilities.
nodejs
Core Node.js backend patterns for TypeScript applications including async/await error handling, middleware concepts, configuration management, testing strategies, and layered architecture principles. Use when building Node.js backend services, APIs, or microservices.
prisma
Prisma ORM patterns including Prisma Client usage, queries, mutations, relations, transactions, and schema management. Use when working with Prisma database operations or schema definitions.
react
Core React 19 patterns including hooks, Suspense, lazy loading, component structure, TypeScript best practices, and performance optimization. Use when working with React components, hooks, lazy loading, Suspense boundaries, or React-specific TypeScript patterns.
skill-developer
Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.
tanstack-query
TanStack Query v5 data fetching patterns including useSuspenseQuery, useQuery, mutations, cache management, and API service integration. Use when fetching data, managing server state, or working with TanStack Query hooks.
tanstack-router
TanStack Router file-based routing patterns including route creation, navigation, loaders, type-safe routing, and lazy loading. Use when creating routes, implementing navigation, or working with TanStack Router.
nexus-tutorial
Use for creating executable Jupyter tutorials and AI engineering walkthroughs with runnable cells. Trigger on requests for step-by-step guides, notebook-based teaching, or shareable code-first learning content. Prioritize reproducibility, clarity, and copy-paste-ready outputs. When in doubt, use this skill.
brian-api
Brian API — natural language to executable Web3 transactions. Convert text intents into swap, bridge, transfer, deposit, withdraw, and borrow transactions across multiple chains. REST API, LangChain integration, and knowledge queries for DeFi protocol data.
coinbase-agentkit
Coinbase AgentKit — build AI agents with onchain capabilities. Wallet creation/management, token transfers, swaps, contract deployment, NFT minting, and ENS registration. Framework integrations with LangChain and Vercel AI SDK. Supports Base, Ethereum, Arbitrum, and Polygon.
goat
GOAT (Great Onchain Agent Toolkit) — 200+ protocol integrations across 30+ chains. Tool creation, framework adapters (AI SDK/LangChain/Eliza), DeFi actions (swap/bridge/transfer), wallet management, and modular plugin architecture for building onchain AI agents.
dare-ax
Agent Experience (AX) — codifica padrões para desenvolvimento assistido por IA em três planos (Discovery, Usage, Defense). Garante que todo projeto DARE exponha sinais estruturados (llms.txt, OpenAPI, --json, rate limit) que agentes de código precisam para trabalhar sem refactor desnecessário.
hacker-news-scraper
Search Hacker News stories and comments using the free Algolia API. No Apify token needed. Use when you need to find HN discussions, track mentions, discover Show HN launches, or monitor tech community sentiment.
dive-into-langgraph
A comprehensive guide and reference for building agents using LangGraph 1.0, including ReAct agents, state graphs, and tool integrations.
agent-governance
Governance, safety, and trust controls for AI agent systems: policy enforcement, intent classification, audit trails, trust scoring. Triggers: /agent-governance, agent safety, tool access control.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no p
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
query-writing
Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.
airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
angular-migration
Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS applications, planning framework migrations, or modernizing legacy Angular code.
api-design-principles
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
architecture-decision-records
Write and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architectural choices, or establishing decision processes.
architecture-patterns
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
auth-implementation-patterns
Master authentication and authorization patterns including JWT, OAuth2, session management, and RBAC to build secure, scalable access control systems. Use when implementing auth systems, securing APIs, or debugging security issues.
changelog-automation
Automate changelog generation from commits, PRs, and releases following Keep a Changelog format. Use when setting up release workflows, generating release notes, or standardizing commit conventions.
code-review-excellence
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
data-storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
dbt-transformation-patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
debugging-strategies
Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
defi-protocol-templates
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
deployment-pipeline-design
Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use when architecting deployment workflows, setting up continuous delivery, or implementing GitOps practices.
e2e-testing-patterns
Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when implementing E2E tests, debugging flaky tests, or establishing testing standards.
error-handling-patterns
Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.
event-store-design
Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.
fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
git-advanced-workflows
Master advanced Git workflows including rebasing, cherry-picking, bisect, worktrees, and reflog to maintain clean history and recover from any situation. Use when managing complex Git histories, collaborating on feature branches, or troubleshooting repository issues.
github-actions-templates
Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.
gitlab-ci-patterns
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.
hybrid-cloud-networking
Configure secure, high-performance connectivity between on-premises infrastructure and cloud platforms using VPN and dedicated connections. Use when building hybrid cloud architectures, connecting data centers to cloud, or implementing secure cross-premises networking.
kpi-dashboard-design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
linkerd-patterns
Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.
microservices-patterns
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
mtls-configuration
Configure mutual TLS (mTLS) for zero-trust service-to-service communication. Use when implementing zero-trust networking, certificate management, or securing internal service communication.
multi-cloud-architecture
Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
nx-workspace-patterns
Configure and optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.
openapi-spec-generation
Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns. Use when creating API documentation, generating SDKs, or ensuring API contract compliance.
postgresql-table-design
Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
saga-orchestration
Implement saga patterns for distributed transactions and cross-aggregate workflows. Use when coordinating multi-step business processes, handling compensating transactions, or managing long-running workflows.
screen-reader-testing
Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues, or ensuring assistive technology support.
service-mesh-observability
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
solidity-security
Master smart contract security best practices to prevent common vulnerabilities and implement secure Solidity patterns. Use when writing smart contracts, auditing existing contracts, or implementing security measures for blockchain applications.
sql-optimization-patterns
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
terraform-module-library
Build reusable Terraform modules for AWS, Azure, and GCP infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
turborepo-caching
Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.
wcag-audit-patterns
Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.
web3-testing
Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, setting up blockchain test suites, or validating DeFi protocols.
workflow-orchestration-patterns
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.
ultrathink_memory
Persistent memory system for UltraThink — search, save, and recall project context, decisions, and patterns across sessions using Postgres-backed fuzzy search with synonym expansion.
ultrathink_review
Multi-pass code review powered by UltraThink's quality gate — checks correctness, security (OWASP), performance, readability, and project conventions in a single structured pass.
skillify
Use when the user wants to take a concept observed in an external tool, library, methodology or article (e.g. Ruflo, LangGraph, BMAD, a blog post, a paper) and turn it into a reusable Claude Code skill — keeping the idea, dropping the rest. Triggers on phrases like "skillify this", "make a skill out of X", "extract this pattern as a skill", or after the user identifies a useful concept while testing a tool.
deserialize
Insecure-deserialization playbook — fingerprint the language/format (Java serialized, .NET BinaryFormatter, Python pickle, PHP unserialize, Node serialize, YAML/JSON-with-types), then build a working gadget chain with ysoserial / ysoserial.net / phpggc / custom pickle. Use when you see serialized blobs (rO0/AC ED, base64 ViewState, PHP O:) or a parameter/cookie that deserializes user input.
graphql
GraphQL pentest playbook — find the endpoint, dump the schema (introspection or field-suggestion fallback), then test for authorization gaps, query batching, alias overload, depth-based DoS, and SQLi/NoSQLi in resolver arguments. Use when the target exposes a /graphql endpoint, GraphiQL, Apollo, or accepts GraphQL queries.
jwt
JWT attack playbook — algorithm confusion (alg=none, HS/RS confusion), kid path traversal/SQLi, jku/x5u SSRF, weak HS256 cracking, and embedded JWK trickery. Use when the target uses JWTs for auth (header.payload.signature).
race
Race condition / TOCTOU playbook — limit overrun (one-time codes used twice, gift cards spent twice), single-packet attack (last-byte sync) to force parallel processing, and state-confusion races (file upload + read, order before payment). Use when timing-sensitive logic could be abused — one-time codes, coupons/gift cards, balance or limit checks, double-spend.
recon
External recon playbook for a web target — subdomain enumeration, live-host probing, tech fingerprinting, and a first pass at content discovery. Use when the user gives you a root domain or apex and wants attack surface mapping.
ssrf
Deep-dive SSRF testing — bypass filters, hit cloud metadata, chain to RCE/credential disclosure. Use when a target parameter clearly accepts a URL or hostname.
ssti
Server-Side Template Injection — fingerprint the engine first (Jinja2 / Twig / Velocity / Freemarker / ERB / Smarty / Mako / Handlebars / Pug), then escalate the engine-specific primitive to RCE or sandbox escape. Use when user input is reflected through a template engine (Jinja2/Twig/Velocity/Freemarker/ERB/Smarty/Mako/Handlebars/Pug) or {{7*7}} evaluates to 49.
supabase
Supabase / PostgREST Row-Level-Security playbook — pull the anon (or leaked service_role) key out of the frontend JS, map tables from the auto-generated OpenAPI spec, test anonymous RLS READ disclosures (PII/secret leaks), and anonymous RLS WRITE abuse (insert/update/delete — e.g. forging "certificate"/verification/entitlement rows the app trusts). Use when the target's frontend talks to *.supabase.co, ships an anon JWT, or you see /rest/v1/, /auth/v1/, /storage/v1/ requests.
takeover
Subdomain takeover playbook — sweep subdomains for dangling CNAMEs / NS records pointing at unclaimed third-party resources (GitHub Pages, S3, Heroku, Azure, Netlify, Shopify, ...), confirm with the engine's HTTP fingerprint, then prove impact by claiming the resource in scope. Use when enumerating subdomains for dangling CNAME/NS records pointing at unclaimed third-party services.
webvuln
Web vulnerability hunting playbook. Use after recon, when you have specific hosts/endpoints to test for IDOR/BAC, injection, auth flaws, SSRF, and known CVEs. Emphasizes real PoC + concrete impact.
model-serving
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
couchbase-ai-applications
Design and build AI-powered applications on Couchbase, including RAG pipelines, vector search architecture, embedding strategies, and AI agent data patterns. Use whenever the user asks about RAG, retrieval-augmented generation, vector search for AI, Hyperscale Vector Index (HVI), Composite Vector Index (CVI), Search Vector Index (SVI), embedding pipelines, semantic search, AI agent memory, grounding LLMs with Couchbase, agentic data patterns, billion-scale vector search, multi-vector search, AI application architecture, or 'how do I build an AI app with Couchbase.' Distinct from couchbase-fts (which covers FTS index mechanics and query syntax) — this skill is about end-to-end AI application design: the data model, embedding pipeline, index type selection, retrieval strategy, and integration with LLM frameworks. Use proactively when the user is building AI features or has a use case involving language models, embeddings, or semantic retrieval.
1password
Access 1Password secrets via the op CLI: read passwords, inject secrets into commands, manage vaults. Requires desktop app integration.
bear-notes
Create, search, and manage Bear notes via the grizzly CLI. Supports tags, x-callback-url, and JSON output.
blucli
Control Bluesound/NAD players: discovery, playback, volume, grouping, and TuneIn radio via blu CLI.
camsnap
Capture snapshots, clips, or motion events from RTSP/ONVIF IP cameras via camsnap CLI.
elevenlabs-tts
Text-to-speech with ElevenLabs: generate realistic voice audio from text. Requires ELEVENLABS_API_KEY.
gemini-image-gen
Generate or edit images with Google Gemini. Alternative to DALL-E for image generation. Requires GEMINI_API_KEY.
google-places
Search for places, restaurants, businesses via Google Places API. Use when user asks about nearby locations. Requires GOOGLE_PLACES_API_KEY.
imessage
Send and read iMessage/SMS via Messages.app using the imsg CLI. List chats, view history, send messages to phone numbers or Apple IDs.
nano-pdf
Edit PDF slides and pages using natural language with Gemini AI. Supports editing existing pages, adding new slides, and style-matching. Requires GEMINI_API_KEY.
obsidian
Work with Obsidian vaults: create, search, move, delete notes. Plain Markdown files with wikilink-aware operations via obsidian-cli.
openai-image-gen
Generate images with DALL-E 3 via OpenAI API. Use when user asks to create or generate images. Requires OPENAI_API_KEY.
openhue
Control Philips Hue lights and scenes: on/off, brightness, color, color temperature, rooms, and scene activation via openhue CLI.
sonos
Control Sonos speakers: discover, play/pause, volume, grouping, favorites, and queue management via sonos CLI.
things
Manage Things 3 on macOS: add/update todos and projects via URL scheme, read inbox/today/upcoming, search tasks. Syncs to iOS.
trello
Manage Trello boards: list/create/move cards, checklists, labels. Requires TRELLO_API_KEY and TRELLO_TOKEN.
weather
Get current weather and forecasts via wttr.in or Open-Meteo. Use when: user asks about weather, temperature, or forecasts for any location. NOT for: historical weather data, severe weather alerts, or detailed meteorological analysis. No API key needed.
kavach
Add a default-deny execution gate around AI-agent actions in Python or Node / TypeScript using the kavach-sdk library. Use when the user is integrating Kavach, wants to add policy enforcement, drift detection, signed permit tokens, signed audit chains, secure channels, or default-deny request validation, mentions Gate, Guarded, PermitToken, ActionContext, EvaluateOptions, McpKavachMiddleware, guardTool, check_tool_call, evaluate_tool_call, or wants to wrap LangChain, LangGraph, MCP tool calls, Express, Fastify, or any agent tool-call code behind a deny-by-default check. Skip if Kavach is already wired up and the user is debugging unrelated code, or if the user is asking about a different policy engine (OPA, Cerbos, Casbin).
agent-orchestration
Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.
langsmith-fetch
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
dcf-valuation
Performs discounted cash flow (DCF) valuation analysis to estimate intrinsic value per share for Japanese listed companies. Triggers when user asks for fair value, intrinsic value, DCF, valuation, "what is X worth", price target, undervalued/overvalued analysis, or wants to compare current price to fundamental value.
x-research
X/Twitter public sentiment research. Searches X for real-time perspectives, market sentiment, expert opinions, breaking news, and community discourse. Use when: user asks "what are people saying about", "X/Twitter sentiment", "check X for", "search twitter for", "what's CT saying about", or wants public opinion on a stock, sector, company, or market event.
skill-creation
Guide for creating, updating, and managing skills. Use when: you need to extend your capabilities with a new skill, or the user asks you to create one. Skills are persistent Markdown instruction modules auto-discovered at runtime.
troubleshooting
Diagnose and resolve errors with Ciana's host bridges, CLI tools, Claude Code mode, macOS permissions, and gateway connectivity.
solana-agent-kit
Comprehensive guide for building AI agents that interact with Solana blockchain using SendAI's Solana Agent Kit. Covers 60+ actions, LangChain/Vercel AI integration, MCP server setup, and autonomous agent patterns.
ralpharchive
Archive the current Ralph prd.json and progress.txt before starting a new feature. Use when you need to archive a completed or abandoned Ralph run. Triggers on: archive ralph, archive prd, ralph archive, clear ralph, reset ralph.
context-doctor
Visualize and diagnose OpenClaw context window usage. Generates a terminal-rendered breakdown showing workspace files (status, chars, tokens), installed skills inventory, and token budget allocation across bootstrap components. Use when: (1) user asks about context window health or token usage, (2) debugging agent quality degradation ("agent got dumber"), (3) after editing workspace files to verify impact, (4) auditing bootstrap overhead. NOT for: conversation history analysis, model selection, or cost tracking.
browser-use
用 Browser-Use 做复杂网页自动化（多步骤登录、填表、发帖、数据抓取）。当内置 browser tool（snapshot→act）搞不定时用这个——它是专门的浏览器AI agent，一个task丢进去自主完成全流程。触发词：browser-use、浏览器自动化、自动登录、自动填表、自动发帖、网页操控、复杂网页操作。
building-langgraph-agents
LangGraph development for stateful multi-agent applications, cyclic workflows, conditional routing, human-in-the-loop patterns, and persistent state management. Use for complex AI orchestration, agent coordination, and production-grade agentic systems.
agent-memory-systems
Architect agent memory across short-term context, long-term vector stores, and CoALA-style cognitive layers, with LangMem patterns, vector-DB selection (Pinecone/Qdrant/Chroma), and chunking strategy. USE WHEN designing the memory architecture and vector-store stack for a production agent.
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.
kuangjia-chaijie
框架/代码库主题深度拆解工作流。输入"框架名 + 主题"（如"Hermes 记忆架构"、"LangChain 工具调用"），按 4 阶段流水线产出种子卡 → 全景图 → 每个子模块的七要素卡 → 跨子模块综合材料。目的是让分享者在写技术文章/做技术分享之前，先完整、清晰、不漏地理解主题。 本 skill 是 agentic 流水线，不是聊天：自顶向下（项目心智模型扫描）+ 自底向上（符号反向追踪）双路并用，关键节点强制人在回路确认范围，每阶段有硬退出标准。 当用户说「我想深入拆解 XX 框架的 YY 模块」「帮我理清 XX 框架的 YY 架构」「我要写一篇关于 XX 框架某部分的文章，先帮我搞清楚」「先给我把 XX 的全景图整出来」「我要做技术分享，把 XX 拆清楚」时使用。
eval-generator
Generates eval test cases for AI agents from an eval suite plan (output of /eval-suite-planner) or a plain-English agent description. Supports both single-response and conversation (multi-turn) evaluation modes. Outputs a test set table, a CSV file in the Copilot Studio import format (single-response only - used here as the primary worked example; trivially adaptable to any harness that accepts tabular test cases), and a docx report for human review.
eval-suite-planner
Produces a concrete eval suite plan for AI agents - grounded in Microsoft's Eval Scenario Library and MS Learn agent evaluation guidance (Copilot Studio is the primary worked example, but the plan is platform-agnostic and adapts to any agent harness). Outputs scenario types, evaluation methods, quality signals, thresholds, and priority order - before any test cases are generated or evals are run.
eval-triage-and-improvement
Use this skill when AI agent evaluations have come back and the user needs to interpret scores, diagnose root causes of underperforming test cases, find remediation steps, or analyze patterns to improve their agent. Works against any agent platform - Copilot Studio is the primary worked example here, but the triage framework applies equally to custom harnesses, LangChain/LangGraph, AutoGen, Semantic Kernel, OpenAI Assistants, and other agent runtimes. Always use this skill when the user mentions: "eval failed", "why did this fail", "triage", "diagnose failure", "low pass rate", "fix evaluation results", "not passing", "failing test cases", "evaluation results", "improve my eval scores", or any situation where eval scores need interpretation and action.
blogwatcher
Monitor RSS/Atom feeds for new articles. Use when user asks to track blogs, news sources, or content updates. No API key needed.
gifgrep
Search for GIFs and animated images. Use when user asks for a GIF, reaction image, or animated content. No API key needed.
video-frames
Extract frames, thumbnails, or clips from video files using ffmpeg. Use when analyzing video content or creating previews.
harness-operator
Load and operate this project-local dual-operator harness. Use when the user says "you are operator" or "you are orchestrator" (equivalent triggers) or asks Claude Code to operate the generated harness.
harness-task-close
Close or block a harness task with evidence, clean state, shared context updates, and regulation review.
building-pydantic-ai-agents
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.
langchain-architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
ai-pm
Reviews and shapes 0→1 PMF-stage product decisions for AI agent infrastructure and product surface — runtime, orchestration, memory, tools, evals, harness design, and agent reasoning interfaces. Activates when the user is sizing an agent capability, choosing between workflow patterns vs autonomous agents, designing tool/skill schemas, building an eval pipeline from scratch, debating framework adoption, writing a PRD/decision-record, designing the harness (tests/docs/specs/observability) that agents operate within, or critiquing the spec layer of an agent product. Anchored on Anthropic Building Effective Agents + Krieger (research-coupling) / Karina Nguyen (eval-as-Schelling-point) / Lopopolo (harness-as-leverage) / Turley (ship-to-understand) / Cherny (prototype-density) PM thought leadership. Does not handle scale-stage tradeoffs (1→10+ enterprise sales, multi-tenant cost, SLA contracts), final architecture authority on infra primitives (defer to staff/principal engineer), agent-safety/red-team review, or B2
ebook-ingest
Use this skill when the user wants to find, download, and prepare an ebook for AI agent ingestion (RAG, fine-tuning, or long-context reference). Triggers include: requests to acquire a digital copy of a book the user owns in print, building a personal book corpus for an AI agent, converting EPUB/PDF/MOBI to clean Markdown for LLM consumption, or chunking books for vector stores. Handles search across multiple sources (Gutenberg, Standard Ebooks, Anna's Archive, LibGen, Z-Library, archive.org), format conversion via calibre/pandoc, OCR for scanned PDFs, cleanup, metadata, and chunking. Do NOT use for academic papers (use Sci-Hub/unpaywall), bulk public-domain scraping (hit Gutenberg's API directly), or DRM'd commercial ebooks the user has not purchased.
openmaic-classroom
OpenMAIC — Open Multi-Agent Interactive Classroom platform for generating immersive AI-powered learning experiences with slides, quizzes, simulations, and multi-agent discussions.
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.
agent-governance
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
java-spring-ai
Use when the user asks to add AI features, integrate Spring AI or LangChain4J, build a chatbot, implement RAG (retrieval-augmented generation), use vector stores, stream LLM responses, or call AI tools/functions in a Spring Boot project.
feishu-access
Manage Feishu/Lark channel access — approve pairings, edit allowlists, set DM policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Feishu channel.
wechat-access
Manage WeChat channel access — approve pairings, edit allowlists, set DM policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the WeChat channel.
wechat-configure
Set up the WeChat channel — scan QR code to login, check channel status. Use when the user asks to configure WeChat, login, or check channel status.
vector-db-init
Interactively initializes the Vector DB plugin. Guided discovery asks which folders to index, confirms the manifest, then scaffolds vector_profiles.json for high-performance In-Process or Native Server connections. Mandatory first step before ingestion or search.
typescript-expert
TypeScript strict mode idioms and modern patterns
agentic-rag-architect
Triggers on keywords RAG, GraphRAG, vector database, agentic RAG, semantic search
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
gitnexus-cli
Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"
gitnexus-debugging
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"
gitnexus-exploring
Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"
gitnexus-guide
Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
gitnexus-impact-analysis
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
gitnexus-refactoring
Use when the user wants to rename, extract, split, move, or restructure code safely. Examples: "Rename this function", "Extract this into a module", "Refactor this class", "Move this to a separate file"
agent-construction
Multi-agent architecture, orchestrator patterns, tool design, agent loops, memory, error handling, handoffs
claude-api
Anthropic Claude API: prompt caching, streaming, tool use, batch processing, model selection, cost optimization
crewai-multi-agent-framework
Building role-based multi-agent workflows in Python; user mentions CrewAI; need a working multi-agent prototype fast; the problem maps naturally to job roles (researcher, writer, reviewer, analyst); s
langgraph
LangGraph stateful agent framework: StateGraph, nodes, edges, conditional branching, persistence/checkpointing, human-in-the-loop, multi-agent subgraphs
prompt-engineering
Prompt engineering for production: few-shot prompting, chain-of-thought, system prompt design, output formatting, temperature settings, and evaluation — for Claude and other LLMs
langchain
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
feishu-configure
Set up the Feishu/Lark channel — configure app credentials, check status. Use when the user asks to configure Feishu, set app_id/app_secret, or check channel status.
arize
Instrument agentic LLM apps built on the Claude Agent SDK (claude-agent-sdk) and/or LangGraph with Arize Phoenix and OpenInference — tracing, evaluation, annotations, experiments, cost tracking, and self-hosting. Use when the user mentions Phoenix, arize-phoenix, openinference, LLM observability, LLM-as-judge evals, tracing Claude Agent SDK `query()` / `ClaudeSDKClient` calls, tool-use observability, tracing LangGraph nodes/edges, or debugging latency/cost/quality of an agent.
langgraph-orchestration
Use this skill for LangGraph, Deep Agents, LangChain agents built on LangGraph, MCP-to-LangGraph tool bridging, stateful workflows, subgraphs, subagents, interrupts, checkpointing, streaming, and multi-agent orchestration. Trigger when code imports langgraph, deepagents, langchain_mcp_adapters, langchain.agents, or when the user asks for agent graphs, orchestration, durable execution, HITL, or LangGraph architecture and patterns.
citation-validator
Use this skill when modifying src/regulaitor/citation/validator.py or its policy. Documents the canonical 3-check validation procedure and the rules for evolving it (e.g. adding a fuzzy-fallback layer in H15).
document-analysis
Use this skill when extracting, sanitizing, segmenting, or analyzing a document end-to-end through the RegulAItor pipeline (PDF or Markdown). Activates the full extract→sanitize→segment→loop[gate→retriever→analyst→auditor]→aggregate flow with SSDLC-aligned defaults.
evals-runner
Use this skill when running the H8 evaluation harness, reading `evals/reports/latest.md`, deciding whether to re-run, or extending the gold set. Activates from H8 onwards.
prompt-versioning
Use this skill when adding, modifying, or rolling back agent prompts in src/regulaitor/agents/prompts/ to keep the project's prompt history reproducible and auditable. Activates from H4 onwards (Analyst, Auditor, Council).
rag-ingest
Use this skill when adding a new regulatory corpus (NIS2, DORA, or any future norma) following the H1 RegulAItor pattern. Ensures the new corpus integrates with the existing fetch/parse/validate/manifest pipeline without ad-hoc divergence.
redteam-runner
Use this skill when running the H9 red team suite, reading `redteam/reports/latest.md`, deciding when to re-run, or extending the attack set. Activates from H9 onwards.
secure-coding-checklist
Use this skill before merging any PR that touches src/regulaitor/security/, src/regulaitor/document/sanitizer.py, src/regulaitor/citation/validator.py, or src/regulaitor/agents/auditor.py. Activates from H9 onwards (CLAUDE.md §12.3.10).
scientific-schematics
Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
vector-db-cleanup
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
vector-db-ingest
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
vector-db-search
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints.
fallow
Codebase intelligence for JavaScript and TypeScript. Free static layer reports quality, changed-code risk, cleanup opportunities (unused files, exports, types, dependencies), code duplication, circular dependencies, complexity hotspots, architecture boundary violations, feature flag patterns, and opt-in security candidates. Runtime coverage merges production execution data into the same health report for hot-path review, cold-path deletion confidence, and stale-flag evidence, with a single local capture available by default and continuous/cloud runtime monitoring available as an optional mode. 118 framework plugins, zero configuration, sub-second static analysis. Use when asked to analyze code health, audit PR risk, find cleanup opportunities or unused code, detect duplicates, check circular dependencies, audit complexity, check architecture boundaries, detect feature flags, surface security candidates, clean up the codebase, auto-fix issues, merge runtime coverage, or run fallow.
ledger
Trade decision audit trail and statistics
zeroclaw
Use when building, configuring, deploying, or troubleshooting ZeroClaw AI agent infrastructure — including provider setup, channel binding, memory backends, config.toml authoring, CLI usage, Docker/native runtime, and migration from other agent frameworks
chatbot-creator-skill
Scaffold a standalone LLM chatbot app (FastAPI + React + Vite). Builds the full project — user auth, multi-turn chat with Claude/OpenAI/Gemini, WebSocket streaming, SQLite persistence. Warns before overwriting existing files.
using-context7
Use when working with any library/framework/SDK to fetch current docs. Beats training-data memory which is stale.
using-context7
Use when working with any library/framework/SDK to fetch current docs. Beats training-data memory which is stale.
browser-use
Use when an AI agent needs to control a browser, automate web tasks, scrape pages, fill forms, or click buttons autonomously. Triggers on: 'browser automation', 'web agent', 'browser-use', 'AI browse', 'tự động duyệt web', 'điều khiển trình duyệt', 'scrape with AI', 'click button automatically', 'fill form automatically', 'web task automation'.
ai-agent-design
Design and implement production-grade AI agents using LangGraph (TypeScript) and Convex as the backend. Use this skill whenever the user wants to build an AI agent, agentic workflow, multi-agent system, LLM pipeline, or anything involving autonomous task execution, tool-calling, ReAct loops, orchestrator-worker patterns, or stateful AI systems — especially when TypeScript, LangGraph, or Convex are mentioned or implied. Also trigger when the user asks about state management for agents, agent memory, human-in-the-loop checkpoints, or connecting LLMs to tools and external APIs in a TypeScript codebase.
observability-telemetry
Benchmarking and telemetry tracking for AI engineering agents using LangSmith.
retrieval-patterns
LangChain Forum RAG System — Support engineer dashboard with AI-powered Q&A, analytics, and LangSmith observability
inquiry_bot
Instructions and architectural analysis of the Universal Inquiry Bot.
Integration detected automatically from skill content. Some results may be false positives.