📖Knowledge Base Skills
Browse the best Knowledge Base skills for Claude Code, Codex, Cursor and other AI agents.
748 skills · Page 1 of 32
llamaindex
★ 15.4kData 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.
llamaindex
★ 62Data 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.
sentence-transformers
★ 15.4kFramework 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.
notion
★ 3.1kNotion API for creating and managing pages, databases, and blocks.
rag-implementation
★ 38Build Retrieval-Augmented Generation (RAG) systems for AI 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.
product-self-knowledge
★ 5Authoritative reference for Anthropic products. Use when users ask about product capabilities, access, installation, pricing, limits, or features. Provides source-backed answers to prevent hallucinations about Claude.ai, Claude Code, and Claude API.
rag
★ 1検索増強生成（RAG）Skill。知識ベースへのドキュメント追加、意味検索、LLM 回答生成をサポート。ドキュメント Q&A、社内知識検索、カスタム知識ベース構築に使用。
building-rag-systems
Build Retrieval Augmented Generation (RAG) systems for AI applications. Use when creating document Q&A systems, knowledge bases, semantic search, or any application combining retrieval with LLM generation. Triggers include "RAG", "vector database", "embeddings", "document chunking", "semantic search", or "knowledge base".
kb
Knowledge base for session continuity. Maintains project documentationwith fixed directory structure (.pmc/docs/) and formats for PRDs, SOPs,tickets (T0000N), patterns, decisions, code maps, research, and status.CRITICAL BEHAVIORS:1. LOOKUP FIRST - Before any task, check kb for existing docs (SOPs, patterns, code maps, status) that may guide the work. Don't reinvent what's documented.2. LEARN = DOCUMENT - When user says "learn X", record it in appropriate doc: - Procedure? → 2-sop/ - Problem/solution? → 6-patterns/ (with Status: open|resolved) - Code flow? → 7-code-maps/ - Research/notes? → 8-research/ - Design decision? → 5-decisions/D###-{name}.md - User clarification? → capture in ticket 5-final.md3. CAPTURE INTENT - When user clarifies requirements or expresses preferences, record in ticket's 5-final.md (Revealed Intent section).4. INBOX - Direct user messages → 0-inbox/ for later processing by /pmc:dev or /pmc:plan.Use when: setting up docs structure, creating/managing t
heme-onc-consultant
Heme/Onc consultant: Rapid clinical decision support for hematology and oncology with multi-level analysis. Provides immediate guidance followed by deep adversarial validation, tumor board simulation with multiple specialties, evidence-based research, and risk-benefit analysis. Use for diagnostic dilemmas, treatment planning, complex cases, or when uncertain about clinical decisions in hematology/oncology.
citations-retrieval
Document citations and RAG (Retrieval-Augmented Generation) patterns for Claude. Activate for source attribution, document grounding, citation extraction, and contextual retrieval.
rag-implementation
RAG (Retrieval Augmented Generation) implementation patterns including document chunking, embedding generation, vector database integration, semantic search, and RAG pipelines. Use when building RAG systems, implementing semantic search, creating knowledge bases, or when user mentions RAG, embeddings, vector database, retrieval, document chunking, or knowledge retrieval.
digital-brain
★ 5.4kThis skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency.
dialectical-mapping-steelmanning
★ 8Use when debates are trapped in false dichotomies, polarized positions need charitable interpretation, tradeoffs are obscured by binary framing, synthesis beyond 'pick one side' is needed, or when users mention steelman arguments, thesis-antithesis-synthesis, Hegelian dialectic, third way solutions, or resolving seemingly opposed principles.
rag-implementer
★ 2Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
backend-rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search in FastAPI backends. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
research
★ 2.2kDocument codebase as-is with thoughts directory for historical context
search-company-knowledge
★ 152Search across company knowledge bases (Confluence, Jira, internal docs) to find and explain internal concepts, processes, and technical details. When Claude needs to: (1) Find or search for information about systems, terminology, processes, deployment, authentication, infrastructure, architecture, or technical concepts, (2) Search internal documentation, knowledge base, company docs, or our docs, (3) Explain what something is, how it works, or look up information, or (4) Synthesize information from multiple sources. Searches in parallel and provides cited answers.
vault-search
★ 79Semantic search and Dataview-style queries across the Obsidian vault. Use when searching for notes by meaning, finding related content, querying frontmatter metadata, or answering questions about vault contents. Trigger phrases include "search vault", "find notes about", "what do I have on", "related notes", "list tasks", "show positions".
setting-detector
★ 36基于关键词自动检测故事设定（类型、时代、主题）并激活相应的知识库 - 在后台静默工作，无需用户干预即可提供相关写作指导
programming-swift
★ 22Provides the complete content of 'The Swift Programming Language (6.2.3)' book by Apple. Use this skill when you need to verify Swift syntax, look up language features, understand concurrency, resolve compiler errors, or consult the formal language reference.
gno
★ 10Search 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.
concept-forge
★ 8Transform nebulous ideas into sharp, testable frameworks through multi-perspective dialectical interrogation. Use when developing vague intuitions, pressure-testing concepts, structuring half-formed frameworks, or distinguishing new ideas from existing concepts. Triggers include "explore this idea," "think through X," or "challenge my thinking."
comprehensive-research
★ 5Multi-phase research orchestration for thorough codebase, documentation, and external knowledge investigation. Invoked by /ai-eng/research command. Use when conducting deep analysis, exploring codebases, investigating patterns, or synthesizing findings from multiple sources.