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Found 2,728 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.
Comprehensive test execution with parallel analysis and coverage reporting. Use when running test suites or troubleshooting failures with the run-tests workflow.
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.
Document Q&A with RAG using Supabase pgvector store.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Brownfield Upgrade - Upgrade all dependencies and modernize the application while maintaining spec-driven control. Runs after Gear 6 for brownfield projects with modernize flag enabled. Updates deps, fixes breaking changes, improves test coverage, updates specs to match changes.
Use when building a thread-safe data persistence layer in Swift using actors with in-memory cache and file storage.
Configure Lakebase for agent memory storage. Use when: (1) Adding memory capabilities to the agent, (2) 'Failed to connect to Lakebase' errors, (3) Permission errors on checkpoint/store tables, (4) User says 'lakebase', 'memory setup', or 'add memory'.
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.
Configure Vitest 4.x with correct pool architecture, coverage settings, and multi-project setup. Use when creating or modifying vitest.config files or setting up test infrastructure.
Android core components lifecycle, Activities, Fragments, Services, Intent system.
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app).