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Found 1,577 Skills
Query decomposition for multi-concept retrieval. Use when handling complex queries spanning multiple topics, implementing multi-hop retrieval, or improving coverage for compound questions.
HyDE (Hypothetical Document Embeddings) for improved semantic retrieval. Use when queries don't match document vocabulary, retrieval quality is poor, or implementing advanced RAG patterns.
Comprehensive test execution with parallel analysis and coverage reporting. Use when running test suites or troubleshooting failures with the run-tests workflow.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
Anthropic's Contextual Retrieval technique for improved RAG. Use when chunks lose context during retrieval, implementing hybrid BM25+vector search, or reducing retrieval failures.
Design AI architectures, write Prompts, build RAG systems and LangChain applications
FlexLayout for React - Advanced docking layout manager with drag-and-drop, tabs, splitters, and complex window management
Under the assumption that the US dollar or a certain currency loses its reserve status and gold becomes the only anchor, deduce the 'implied gold price that the balance sheet can withstand' by dividing central bank monetary liabilities by gold reserves, and output the leverage level, gap and ranking of each country or currency.
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.
MinIO S3-compatible object storage API. Use this skill for file upload, download, bucket management, and pre-signed URL generation.
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.
Use when `spec.md`, `plan.md`, and `tasks.md` exist and you need a read-only Spec Kit audit for consistency, requirement-to-task coverage, ambiguity, duplication, or constitution conflicts before implementation.