rag-implementation
wshobsonBuild 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.
embedding-strategies
wshobsonSelect and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
similarity-search-patterns
wshobsonImplement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
rag-engineer
davila7Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
exa-search
davila7Semantic search, similar content discovery, and structured research using Exa API
sentence-transformers
davila7Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
rag-implementation
davila7Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
chroma
davila7Open-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.
pinecone
davila7Managed 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.
literature-review
davila7Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
qdrant-vector-search
davila7High-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.
similarity-search-patterns
sickn33Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
sickn33Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
vexor
sickn33Vector-powered CLI for semantic file search with a Claude/Codex skill
rag-implementation
sickn33Build 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
sickn33Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
AgentDB Vector Search
ruvnetImplement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
literature-review
K-Dense-AIConduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
archon
coleam00Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.
rag-architect
JeffallanUse when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
leann-search
parcadeiSemantic search across codebase using LEANN vector index
para-second-brain
openclawOrganize your agent's knowledge using PARA (Projects, Areas, Resources, Archive) — then make it ALL searchable. The symlink trick enables full semantic search across your entire knowledge base, not just MEMORY.md. Includes session transcript indexing and memory flush protocol. Your agent finally has a real second brain.
web-search-plus
openclawUnified search skill with Intelligent Auto-Routing. Uses multi-signal analysis to automatically select between Serper (Google), Tavily (Research), Exa (Neural), You.com (RAG/Real-time), and SearXNG (Privacy/Self-hosted) with confidence scoring.
local-rag-search
openclawEfficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.
literature-review
openclawAssistance with writing literature reviews by searching for academic sources via Semantic Scholar, OpenAlex, Crossref and PubMed APIs. Use when the user needs to find papers on a topic, get details for specific DOIs, or draft sections of a literature review with proper citations.
memory-system-v2
openclawFast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events. Use when you need persistent memory across sessions or want to recall prior work/decisions.
memory-manager
openclawLocal memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
reddit-insights
openclawSearch and analyze Reddit content using semantic AI search via reddit-insights.com MCP server. Use when you need to: (1) Find user pain points and frustrations for product ideas, (2) Discover niche markets or underserved needs, (3) Research what people really think about products/topics, (4) Find content inspiration from real discussions, (5) Analyze sentiment and trends on Reddit, (6) Validate business ideas with real user feedback. Triggers: reddit search, find pain points, market research, user feedback, what do people think about, reddit trends, niche discovery, product validation.
qmd
openclawSearch and retrieve markdown documents from local knowledge bases using qmd. Supports BM25 keyword search, vector semantic search, and hybrid search with LLM re-ranking. Use for querying indexed notes, documentation, meeting transcripts, and any markdown-based knowledge. Requires qmd CLI installed (bun install -g https://github.com/tobi/qmd).
read-github
openclawRead GitHub repos the RIGHT way - via gitmcp.io instead of raw scraping. Why this beats web search: (1) Semantic search across docs, not just keyword matching, (2) Smart code navigation with accurate file structure - zero hallucinations on repo layout, (3) Proper markdown output optimized for LLMs, not raw HTML/JSON garbage, (4) Aggregates README + /docs + code in one clean interface, (5) Respects rate limits and robots.txt. Stop pasting raw GitHub URLs - use this instead.
tos-vectors
openclawManage vector storage and similarity search using TOS Vectors service. Use when working with embeddings, semantic search, RAG systems, recommendation engines, or when the user mentions vector databases, similarity search, or TOS Vectors operations.
gno
openclawSearch local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs RAG/semantic search, or wants to start a local web UI for their docs.
azure-search-documents-ts
microsoftBuild search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
azure-search-documents-dotnet
microsoftAzure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET", "SearchClient", "SearchIndexClient", "vector search C#", "semantic search .NET", "hybrid search", "Azure.Search.Documents".
azure-search-documents-py
microsoftAzure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
azure-ai
microsoftUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. USE FOR: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech. DO NOT USE FOR: Function apps/Functions (use azure-functions), databases (azure-postgres/azure-kusto), general Azure resources.
pgvector-semantic-search
timescaleUse this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
postgres-hybrid-text-search
timescaleUse this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET", "SearchClient", "SearchIndexClient", "vector search C#", "semantic search .NET", "hybrid search", "Azure.Search.Documents".
azure-search-documents-py
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
cursor-codebase-indexing
Execute set up and optimize Cursor codebase indexing. Triggers on "cursor index setup", "codebase indexing", "index codebase", "cursor semantic search". Use when working with cursor codebase indexing functionality. Trigger with phrases like "cursor codebase indexing", "cursor indexing", "cursor".
mistral-core-workflow-b
Execute Mistral AI secondary workflows: Embeddings and Function Calling. Use when implementing semantic search, RAG applications, or tool-augmented LLM interactions. Trigger with phrases like "mistral embeddings", "mistral function calling", "mistral tools", "mistral RAG", "mistral semantic search".
osgrep
Ryandonofrio3Semantic code search. Use alongside grep - grep for exact strings, osgrep for concepts.
systematic-literature-review
huangwb8Used when users need to conduct a systematic review/literature review/related work/literature research: AI self-defined search terms, multi-source retrieval → deduplication → AI reads and scores each piece (1-10 points for semantic relevance and sub-topic grouping) → select documents based on high score priority ratio → automatically generate 'review' word count budget (70% cited sections + 30% uncited sections, three samples for average) → senior domain experts write freely (fixed abstract/introduction/sub-topics/discussion/prospects/conclusion), retaining strict checks on the number of words and references, forcing export to PDF and Word. Supports multilingual translation and intelligent compilation (en/zh/ja/de/fr/es).
semtools
massgenThis skill provides semantic search capabilities using embedding-based similarity matching for code and text. Enables meaning-based search beyond keyword matching, with optional document parsing (PDF, DOCX, PPTX) support.
google-gemini-file-search
jezwebBuild document Q&A with Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats, query with natural language. Use when: document Q&A, searchable knowledge bases, semantic search. Troubleshoot: document immutability, storage quota (3x), chunking config, metadata limits (20 max), polling timeouts, displayName dropped (Blob uploads), grounding lost (JSON mode), tool conflicts (googleSearch + fileSearch).
google-gemini-embeddings
jezwebBuild RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
cloudflare-vectorize
jezwebBuild semantic search with Cloudflare Vectorize V2. Covers async mutations, 5M vectors/index, 31ms latency, returnMetadata enum changes, and V1 deprecation. Prevents 14 errors including dimension mismatches, TypeScript types, testing setup. Use when: building RAG or semantic search, troubleshooting returnMetadata, V2 timing, metadata index, dimension errors, vitest setup, or wrangler --json output.