Model Context Protocol server exposing 53 tools, 3 resource types, and 3 prompts for AI agent consumption — quoting, building transactions, fee management, analytics, AMM…
Operate Puppetmaster multi-agent orchestrator via MCP verbs (edit, swarm, implement, route, monitor).
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLM — from…
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLM — from…
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLM — from…
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelin — from…
Use when asked to quickly create a QA agent for an API from project rules, reusable prompts, skills, memory/context files, evals, test harnesses, and guardrails.
Run an agent-driven QA pass against a PR preview URL using the kmikeym v0.01m SOP. Drives chrome-devtools MCP through cold sign-up, first prompt, in-app exploration, edit/theme…
Run QA tests using agent-browser and post results to the qabot dashboard. Interactive mode helps craft fixtures.
企查查官方 CLI — 企业工商、风险、知识产权、经营信息一站式查询。输入企业名称即可调用 67 个 API 获取注册信息、股东、高管、失信、行政处罚、专利、招投标、融资、舆情等全维度企业数据。触发场景：查企业、背调、尽调、风险排查、企业画像、商机分析、企业信用评估、供应商审查。使用 agent-browser 补充高管履历和地方互动等互联网信息。
An official Qdrant MCP server implementation that provides semantic memory capabilities for AI agents.
Qdrant Operations — управление коллекциями Qdrant, sparse vectors, snapshots. ИСПОЛЬЗУЙ когда создаёшь/настраиваешь коллекции Qdrant, мигрируешь с ChromaDB, настраиваешь named…
AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles. — from proffesor-for-testing/agentic-qe
Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.
Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance.
Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.
Use Fabric RTI MCP when an agent needs tool-callable access to Microsoft Fabric Real-Time Intelligence services such as Eventhouse, Eventstreams, Activator, and Map instead of…
Connect MCP-compatible agents to Neo4j so they can inspect graph schemas, run Cypher queries, manage graph memory, and operate Aura instances from chat.
Give MCP-capable agents a live global-intelligence surface for country briefs, risk scores, conflict, cyber, market, weather, and infrastructure monitoring.
DEPRECATED — permissions moved to nix-claude-code; edit data/permissions/*.nix there instead of this repo's JSON
MCP connector for QuickBooks – enables AI-powered accounting, invoice management, and financial reporting through Claude
Fast single-pass writing review against Ben Church's writing standards. Use when the user asks to "quickcheck", "quick review", "fast check", "quick edit", "scan my writing", or…
Use the bundled r2mcp MCP server for binary analysis, disassembly, and reverse-engineering tasks.
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search. — from majiayu000/claude-skill-registry
Add an indexing source — add it to settings.json `sources` and immediately do a partial index. Utterance examples "rag add source docs/", "rag-add-source notes/", "register wiki/…
Use when building RAG systems, implementing semantic/hybrid search, selecting vector databases, tuning retrieval quality, or choosing chunking and embedding strategies.
Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying re — from…
RAG pipeline design — document chunking, embedding strategies, retrieval optimization, and answer generation
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API.
RAG-grounded code generation with source citations. Triggers on: grounded code, ground this, cite sources, show me with sources, how do I with attune, reference attune docs,…
Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
Improve RAG corpus quality by adding missing docs, rewriting weak chunks, and filling retrieval gaps.
Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
Production-grade RAG (Retrieval-Augmented Generation) system design patterns from OpenClaw. Use when implementing semantic search, vector retrieval, memory systems, or knowledge…
Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG…
Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking,…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this…
Batch-evaluate rag index quality with pre-registered probes — search results + the active AI session's diagnosis and tuning suggestions.
Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision,…
N-hop graph exploration around a specific entity — reports what is connected to what as a visual/table.
Create a rag index snapshot tarball (vec.db + graph/ + manifest + settings + compatibility header). Example utterances "make a rag snapshot", "rag-export ~/Downloads/foo.tar.gz",…
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HN — from…
Record 👍/👎 feedback on the most recent search results to improve the quality of future searches. Example utterances "this result was good", "rag feedback positive", "that last…
Aggregates accumulated search feedback to report what is searched most, which sources match well, and weak spots (sources with many 👎), and proposes improvements.
Scrape external documents (Confluence, Google Drive, Notion, etc.) via installed official connectors and put them into the local index.
Keep a retrieval corpus current as source documents change, with incremental updates, deletion propagation, and a stated staleness budget.
Full usage guide for the rag plugin — one-line concept, quick start, index of 17 skills, FAQ, troubleshooting, Windows/macOS install.
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
Design and implement Retrieval-Augmented Generation systems — chunking strategy, embedding selection, vector store setup, retrieval pipeline, re-ranking, and evaluation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.