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Found 2,757 Skills
Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.
Comprehensive testing specialization covering test strategy, automation, TDD methodology, test writing, and web app testing. Use when setting up test infrastructure, writing tests, implementing TDD workflows, analyzing coverage, integrating tests into CI/CD, or testing web applications with Playwright. Framework-agnostic approach with framework-specific guidance via reference files.
Generate OpenHarmony C++ unit tests following HWTEST_F framework conventions. Supports Mock strategies, BUILD.gn configuration, NDK/NAPI interfaces, and maintains 75%+ coverage requirements with strict code style consistency. Use when generating unit tests for OpenHarmony C++ source files.
Store objects with R2's S3-compatible storage on Cloudflare's edge. Use when: uploading/downloading files, configuring CORS, generating presigned URLs, multipart uploads, managing metadata, or troubleshooting R2_ERROR, CORS failures, presigned URL issues, or quota errors.
Prepares and audits high-quality datasets for AI/RAG applications. Cleans noise, structure data, and ensures privacy compliance in knowledge bases.
Expert at analyzing documentation quality, coverage, and completeness. Auto-invokes when evaluating documentation health, checking documentation coverage, auditing existing docs, assessing documentation quality metrics, or analyzing how well code is documented. Provides frameworks for measuring documentation effectiveness.
Testing procedures. Invoke with /tzurot-testing for test execution, coverage audits, and debugging test failures.
Convert mixed-format datasheets and hardware reference files (PDF, DOCX, HTML, Markdown, XLSX/CSV) into normalized Markdown knowledge files for AI coding agents. Use when a user asks to ingest datasheets, register maps, pinout/timing sheets, revision histories, or internal hardware notes before searching datasheet content or generating code. Produce RAG-ready section chunks, anchors, image references, and metadata under .context/knowledge.
Semantic and multi-modal search across documents using LanceDB vector embeddings. Use when searching knowledge bases, finding information semantically, ingesting documents for RAG, or performing vector similarity search. Triggers on "search documents", "semantic search", "find in knowledge base", "vector search", "index documents", "LanceDB", or RAG/embedding operations.
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
A skill that equips you with real-time, source-grounded web search and content retrieval using the Exa API—optimized for balanced relevance and speed (type="auto") and full-text extraction for downstream reasoning, RAG, and code assistance. Powering agents with fast, high-quality web search by Exa.AI.
Review PyTorch pull requests for code quality, test coverage, security, and backward compatibility. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".