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Found 47 Skills
Use when you need legal PDF to markdown extraction plus clause chunking and embedding prep; pair with addon-rag-ingestion-pipeline and architect-python-uv-batch.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Use when crawling web pages, extracting markdown content, or scraping website data with intelligent chunking and skeleton planning. Use when the user provides a URL or link to fetch or crawl.
Explains JavaScript bundling, code splitting, chunking strategies, tree shaking, and build pipelines. Use when optimizing bundle size, understanding how modern build tools work, configuring Webpack/Vite/esbuild, or debugging build output.
Document chunking implementations and benchmarking tools for RAG pipelines including fixed-size, semantic, recursive, and sentence-based strategies. Use when implementing document processing, optimizing chunk sizes, comparing chunking approaches, benchmarking retrieval performance, or when user mentions chunking, text splitting, document segmentation, RAG optimization, or chunk evaluation.
Recursive Language Model context management for processing documents exceeding context window limits. Enables Claude to match Gemini's 2M token context capability through chunking, sub-LLM delegation, and synthesis.
Proactive token budget assessment and task chunking strategy. Use this skill when queries involve multiple large file uploads, requests for comprehensive multi-document analysis, complex multi-step workflows with heavy research (10+ tool calls), phrases like "complete analysis", "full audit", "thorough review", "deep dive", or tasks combining extensive research with large output artifacts. This skill helps assess token consumption risk early and recommend chunking strategies before beginning work.
Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
Extract text and data from PDF documents
tokenization과 context window를 중심으로 긴 입력 처리 한계와 실무 대응 방법(분할, 요약, 우선순위화)을 학습시키는 모듈.