Weaviate
DatabaseCommonly used with
Skills using Weaviate (67)
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
faiss
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
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.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
faiss
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
haystack-pipeline
Haystack NLP pipeline configuration for document processing and QA
weaviate-integration
Weaviate vector database setup with GraphQL queries and hybrid search
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
faiss
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
retrieval
Retrieval - vector DBs, embeddings, hybrid search, reranking.
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.
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.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
using-weaviate
Weaviate vector database for semantic search, hybrid queries, and AI-native applications. Use for embeddings storage, similarity search, RAG pipelines, and multi-modal retrieval.
accessibility-checker
Quick A11y review - WCAG 2.1 checklist, screen reader compatibility, keyboard navigation, color contrast
ai-image-prompting
Crafts production-grade prompts for AI image generation (Midjourney, Flux, SDXL, Firefly, Imagen, ComfyUI workflows) — subject, composition, lighting, style references, negative prompts, ControlNet hints. Use when the goal is on-brand, repeatable imagery rather than a one-off lucky generation.
ai-model-selector
Quick guidance on choosing AI models (LLM/VLM/Embedding) based on task, VRAM, cost, and quality requirements
ai-prompting
Quick tips and templates for effective prompt engineering - few-shot examples, chain-of-thought patterns, constraint specification, output formatting
api-designer
Expert guidance on API design including REST vs GraphQL vs gRPC selection, endpoint patterns, authentication strategies, and versioning
architect
Design complex system architectures, evaluate tradeoffs, and make critical technical decisions requiring deep reasoning
architecture-consultant
Expert guidance on cross-domain architecture decisions, technology selection, and infrastructure design requiring deep analysis of tradeoffs and long-term implications
batch-image-pipeline
Writes batch image and video processing scripts (Pillow, ImageMagick, ffmpeg) for asset pipelines — generate size variants, format conversions, colorspace transforms, watermarks, optimization. Use when one design output needs to become 40 deliverables, or when an asset library needs cleanup.
build-vs-buy-decision
Helps a solo founder or small team decide whether to build a feature in-house, buy/integrate a SaaS, or defer it. Considers MRR stage, team capacity, vendor lock-in risk, ongoing maintenance cost, and the "default to building" indie bias. Invoked when the user asks "should we build or buy [feature]", "is it worth integrating [tool]", "should we replace [vendor] with our own implementation", or "what's the cheapest way to ship [capability]".
code-review-expert
Deep code analysis identifying subtle bugs, security issues, performance problems, and architectural concerns requiring expert-level reasoning
consulting-due-diligence
Runs structured technical due diligence on a vendor, acquisition target, or strategic partner; produces a risk-ranked report with findings, evidence, and a recommendation
consulting-incident-coordinator
Coordinates multi-channel incident response for a consulting engagement - drafts war-room updates, status-page entries, client comms, and the post-incident review; use when a client production issue is active or just resolved
consulting-portfolio-status
Turns a directory of per-client engagement files into a board-ready portfolio status report; use when preparing a weekly digest, a steering-committee briefing, or any multi-engagement roll-up
content-calendar-planner
Builds a 30/60/90-day cross-platform content calendar for a vendor across Instagram, TikTok, LinkedIn, X, YouTube Shorts, and newsletter. Takes a theme and cadence as input, produces a dated calendar with hooks, hashtags, CTAs, and a repurposing graph showing which posts feed which platforms. Use when the user says "plan content for next month/quarter", "build a content calendar", or "I need 30 days of posts".
context
Efficient context state inspection, task lifecycle management, and session tracking
database-advisor
Expert guidance on database design, schema optimization, query performance, and database technology selection
deployment-advisor
Deployment strategy guidance - platform selection, CI/CD pipeline design, environment configuration, monitoring
explore-codebase
Systematic codebase onboarding. Builds a mental model of a new or unfamiliar project by exploring structure, architecture, key data models, entry points, and auth patterns.
extract-docs
Systematically extract knowledge from scattered documentation to prevent catastrophic forgetting. Creates structured extraction reports with status tags.
fix-issue
Investigate and fix a GitHub issue or bug report. Reads the issue, reproduces it, identifies root cause, implements fix, adds regression test.
gui-test
Automated visual testing with Playwright MCP - test web apps, presentations, websites, and documents with scalable reviewer perspectives
gui-ux-expert
Quick GUI/UX/UI design consultations and recommendations
hardware-calculator
Quick VRAM/RAM calculations, hardware recommendations, feasibility checks for AI models
idempotency-keys
Designs the idempotency strategy for a state-changing operation - key derivation, storage choice, TTL, collision handling, and threading through downstream side effects. Use when adding a new endpoint with side effects, hardening an existing one, or designing a workflow that survives retries
interview
Interview the user using AskUserQuestion to discover requirements for a feature or task. Probe technical implementation, UX, edge cases, and constraints. Writes final spec to SPEC.md.
kg-research
Research using ONLY knowledge graph semantic search (no file tools, forces KG-first approach)
photoshop-scripting
Writes Adobe Photoshop automation scripts in UXP (JavaScript) or legacy ExtendScript (JSX), and GIMP scripts in Python (Script-Fu fallback). Use when a repetitive Photoshop task needs to run on dozens of files, when a custom panel/plugin is wanted, or when an Action recorder won't capture the logic needed.
react-patterns
React best practices - component patterns, state management selection, performance optimization, testing strategies
repro-audit
Audits a scientific project for reproducibility — environment pinning, seed setting, data hashing, notebook discipline, workflow orchestration, and provenance capture. Use when the user asks "is this project reproducible", "what's missing for a reviewer to rerun this", "audit my repo before submission", "why do I get different results each run", or before submitting code with a manuscript.
saas-metrics-health-check
Performs a diagnostic on a SaaS product's revenue and retention metrics from a CSV or spreadsheet of customers/subscriptions/events. Computes MRR, ARR, churn (gross/net), LTV, CAC, payback period, quick ratio, and cohort retention; flags anomalies; identifies the single highest-leverage fix. Invoked when the user asks "are my SaaS metrics healthy", "compute LTV for my data", "is my churn high", "review my MRR breakdown", or shares a billing/customer CSV.
saas-pricing-strategist
Analyses a SaaS product's pricing page (current and competitor) and produces a concrete redesign with tier structure, value metric, anchoring, annual-discount math, and a measurable rollout plan. Invoked when the user asks "is my pricing right", "review my pricing page", "compare my pricing to [competitor]", "how should I raise prices", "should I add a third tier", or shares a competitor pricing URL with intent to react.
security-reviewer
Cross-layer security analysis (frontend XSS/CSRF, backend injection, AI prompt injection, infrastructure)
slo-designer
Designs SLIs, SLOs, and multi-window multi-burn-rate alerts from a service description, then emits the Prometheus recording rules and alerting rules. Invoke when a service needs its first SLO, when an existing threshold-alert is flapping or missing real incidents, or when an SRE team is operationalising error budgets.
stats-consult
Recommends the appropriate statistical test given a data description and research question, including assumption checks, alternatives if violated, sample-size guidance, and effect-size reporting. Use when the user asks "what test should I use", "is this t-test the right choice", "how many subjects do I need", "what's the right way to analyse this dataset", or describes a dataset + hypothesis without a chosen method.
structured-output-extraction
Builds a reliable LLM-powered extraction pipeline for messy inputs (PDFs, emails, transcripts, HTML) into a strict JSON schema with validation, automated correction loop, and observability. Use when designing extraction from unstructured documents or hardening one that fails too often
task-breakdown
Break complex features into implementable tasks with estimates, dependencies, and risk assessment
terraform-plan-reviewer
Reviews Terraform/OpenTofu plan output for destructive changes, drift, IAM expansions, hardcoded values, and unsafe resource recreations before apply. Invoke when the user shares plan output, when a CI plan job posts a diff to a PR, or before any non-trivial production apply.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
context-compress
Guide for using /compact (Claude Code built-in) with the pre-compact save pipeline. Shows what gets saved before compression and what gets reinjected after.
doc-template
Documentation templates - README, API docs, ADRs, user guides
rag-poisoning
Expert methodology for attacking Retrieval-Augmented Generation (RAG) pipelines through document poisoning, index corruption, adversarial queries, and retrieval manipulation. For authorized red team assessments of AI search and Q&A systems.
genai-integration
Expert guidance for integrating GenAI models, workflows, and observability into applications. (use when designing or implementing LLM/agent/RAG integrations)
Integration detected automatically from skill content. Some results may be false positives.