Qdrant
DatabaseCommonly used with
Skills using Qdrant (65)
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.
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
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
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.
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.
qdrant-integration
Qdrant vector database with filtering, payloads, and quantization support
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.
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.
cocoindex
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
index
Reindexes KB for semantic search via vector store (Qdrant). Triggers: reindex KB, rebuild index, vector reindex, refresh embeddings.
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.
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.
onboarding
First-run onboarding — guides new users through Genesis setup on their first CC session. Configures user profile, essential API keys, Telegram, GitHub backup, and service verification. Triggered automatically when ~/.genesis/setup-complete is absent. Re-runnable by asking Genesis to "run setup" or "reconfigure [section]".
codebase-exploration
Explore and understand codebases using SocratiCode semantic search, dependency graphs, and context artifacts. Use when exploring code, understanding architecture, finding functions/types, analysing dependencies, searching database schemas or API specs, or when socraticode/codebase_search tools are available. Activates when the user asks about code structure, wants to find where a feature lives, or needs to understand how code is organised.
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.
ai-engine-wordpress-mcp-server-and-ai-automation
AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.
building-rag-systems
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
cocoindex
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
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.
cocoindex
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
ccc-data
complete data ecosystem — 8 skills in one. Data pipelines, SQL optimization, visualization, machine learning, data quality, analytics, reporting, and vector search.
ai-engineer
AI/LLM Application Engineer (/ai) — builds LLM-powered product features: RAG, agentic workflows, prompt engineering, tool use, structured output, evals, and guardrails. Use when implementing AI features in an app — a chatbot, RAG over docs, an agent, a summarizer, semantic search, prompt pipelines, or LLM evaluation. Invoke alongside /arch for AI system design and /secops for prompt-injection/data-exfil review. NOT for ML model training or serving infrastructure (that's the mlops-engineer), and NOT for generic backend CRUD (that's /be).
kai
Kai — Self-Improving Meta-Agent that detects recurring patterns in the file-based learnings store (.aidevteam/learnings/, written by /retro) and proposes permanent SKILL.md updates for human review. Clusters by target skill + theme; the Qdrant learnings/agent-knowledge collections are an optional overlay.
backend-fastapi
Documentation for the FastAPI backend, endpoints, and dependency injection.
chatbot-implementation
Details of the RAG Chatbot, including UI and backend logic.
deployment-build
Knowledge of the Vercel deployment pipeline, hybrid build scripts, and environment configuration.
rag-pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
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
sis-memory-orchestrator
Substrate-aware memory orchestration for Starlight Intelligence System. Coordinates a 7-subagent team that proactively manages memory writes, retrieval, knowledge-graph maintenance, privacy redaction, decay, audit, and substrate benchmarking across pluggable backends (mempalace, Letta, Mem0, AgentDB, Qdrant, screenpipe, filesystem). Use when the user asks about SIS memory architecture, wants to add or swap a memory substrate, runs `/sis memory`, asks "where should this capture go", reports memory leaks or staleness, requests benchmarks across substrates, or sets up a new sovereign user adopting SIS. Honors privacy-by-default (everything local; embeddings local; PII redaction before any external call).
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
agenticx-quickstart
AgenticX zero-to-hero quickstart guide. Use when the user wants to get started with AgenticX, create their first project, build their first agent, or run their first workflow. Covers installation, project scaffolding, agent creation, task execution, and CLI basics.
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
agenticx-quickstart
AgenticX zero-to-hero quickstart guide. Use when the user wants to get started with AgenticX, create their first project, build their first agent, or run their first workflow. Covers installation, project scaffolding, agent creation, task execution, and CLI basics.
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
agenticx-quickstart
AgenticX zero-to-hero quickstart guide. Use when the user wants to get started with AgenticX, create their first project, build their first agent, or run their first workflow. Covers installation, project scaffolding, agent creation, task execution, and CLI basics.
note
Append a timestamped mid-session note to the session journal — finding/decision/summary tags for across-compaction memory. Use when you discover something the session must remember past /compact. Folded to the handoff at session end.
dare-rust-workspace
Decisão e migração de Cargo workspace multi-crate para projetos Rust/Axum. Use durante design/blueprint para decidir o layout, ou quando um projeto single-crate cresceu além do que comporta confortavelmente. Cobre os 2 cenários — escolher na fase de design + migrar projeto existente — com critérios objetivos e plano em PRs.
grepai-storage-qdrant
Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.
database-ops
【数据库运维】数据库设计与运维全流程。触发时机：用户说"设计数据库"、"建表"、"数据库选型"、"生成索引"、"迁移脚本"时。
using-vector-databases
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
codebase-search
Searches the codebase by concept using semantic search. Use for exploratory questions about behavior, flow, or architecture rather than exact symbol lookups.
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-reflect
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
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.
shrink-vector-store
Shrink an embedding/RAG vector store 4–32× via int8 or binary quantization with a float rescore pass, preserving recall and provenance metadata. Use when a vector store is too large to be laptop-resident, query cost/latency is too high, or embeddings need to be quantized for FAISS/Qdrant/usearch. Do NOT use for embed-time ingestion failures (e.g. a provider 'too many tokens' 400 — that is an upstream chunking bug, not a storage-size problem) and do NOT enable the dark TurboQuant 4-bit path without the gate below.
ms-agent-framework-rag
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C#. Use when creating RAG applications with semantic search, document indexing, and intelligent agent orchestration. Includes scaffolding scripts, reference implementations, and documentation for vector databases, embedding models, and multi-agent workflows.
rag-debug
Diagnose RAG pipeline issues — check Qdrant collection, embedding provider, MinIO storage, and recent chunk uploads
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.
axon
Use whenever the user wants to crawl, scrape, or extract a website; ingest a GitHub repo, Reddit, YouTube, or local AI sessions; embed content into Qdrant; run semantic search; ask grounded RAG questions; or manage axon's async job queues. Also use when the user mentions axon, the crawler, hybrid search, Qdrant, Tavily, or the MCP tool surface.
rag-specialist
Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
ask
Use when the user wants to ask a question and get an LLM-synthesized answer grounded in indexed documents, do RAG over previously crawled or embedded content, get cited answers from the knowledge base, or find information that was previously indexed. Triggers on "ask axon", "what does the documentation say about", "according to what I've indexed", "RAG query", "use axon to answer", or any question where the user wants grounded answers from indexed content rather than hallucination.
dr
Use when the user wants to check if axon services are healthy, diagnose connectivity problems, verify Qdrant/TEI/Chrome are reachable, troubleshoot why axon isn't working, or run a health check. Triggers on "axon doctor", "check axon health", "is axon working", "troubleshoot axon", "why is axon failing", "check services", "health check", "can axon connect to". Always run this first when something seems broken.
embed
Use when the user wants to embed a local file, directory, or URL into Qdrant; index local documents or code into the RAG; add files from disk to the knowledge base; or re-embed stale content. Triggers on "embed this file", "index this directory", "add to Qdrant", "embed local files", "embed this folder", "index my docs", "add this PDF", "embed into the knowledge base". Different from scrape/crawl (which fetches from web) — embed indexes content already on disk or from a URL directly.
ingest
Use when the user wants to index a GitHub repository, ingest a Reddit subreddit or thread, index a YouTube video or playlist, or import past Claude/Codex/Gemini session transcripts into axon. Triggers on "ingest this repo", "index this GitHub repo", "add this Reddit thread", "ingest subreddit", "index YouTube video", "import my sessions", "ingest GitHub", "index r/", "add this repo to axon". Also use when the user wants to make source code searchable via RAG.
query
Use when the user wants to do a semantic vector search over indexed content, find relevant chunks matching a query, search the Qdrant knowledge base, or get raw search results without LLM synthesis. Triggers on "search axon", "query the knowledge base", "find chunks about", "vector search for", "semantic search", "what's indexed about", "find relevant passages". Different from `ask` (which synthesizes an answer) — query returns raw matching chunks with scores.
retrieve
Use when the user wants to fetch all stored chunks for a specific URL from Qdrant, get everything indexed from a particular page, or see what was stored for a specific source. Triggers on "retrieve from axon", "get the indexed content for this URL", "fetch stored chunks for", "what did axon store from", "show me what's indexed at". Different from query (keyword search) — retrieve fetches by exact URL.
scrape
Use when the user wants to scrape a single URL or a few URLs to markdown, fetch a page's content, extract text from a web page, or save a URL's content into Qdrant. Triggers on "scrape this URL", "fetch the content of", "get the text from this page", "save this page to axon", "read this webpage into the RAG", or when the user pastes a URL and wants its content extracted. Prefer this over crawl when only specific pages are needed rather than a whole site.
search
Use when the user wants to search the web via Tavily and index the results, find recent information on a topic and store it, or combine live web search with automatic crawling. Triggers on "search the web for", "find recent articles about", "search and index", "Tavily search", or when the user wants to pull fresh web content into axon. Different from `query` — this searches the live web, not already-indexed content.
stats
Use when the user wants to see Qdrant collection statistics, check how many points or vectors are indexed, see collection size, or get an overview of the vector store. Triggers on "axon stats", "how many vectors", "collection size", "how many points in Qdrant", "vector store stats", "how much is indexed", "collection stats". Different from sources (URL list) and status (job queue).
Integration detected automatically from skill content. Some results may be false positives.