docs-seeker
Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel
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2025年10月26日 11:35
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相关技能
dbt-data-transformation
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Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows
test-case-designer
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Designs comprehensive test cases for PHP/CakePHP applications, categorizing them into unit, integration, and system tests with proper documentation format
testing-skills-with-subagents
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Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
writing-skills
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Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
test-validator
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Validates PHP test files for CakePHP projects, ensuring compliance with testing standards including proper documentation format, Configure::read usage, and avoiding prohibited patterns
google-gemini-embeddings
jezweb
This skill provides complete coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, document clustering, and similarity matching. Use when implementing vector search with Google's embedding models, integrating with Cloudflare Vectorize, or building retrieval-augmented generation systems. Covers SDK usage (@google/genai), fetch-based Workers implementation, batch processing, 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, etc.), dimension optimization (128-3072), and cosine similarity calculations. Prevents 8+ embedding-specific errors including dimension mismatches, incorrect task types, rate limiting issues (100 RPM free tier), vector normalization mistakes, text truncation (2,048 token limit), and model version confusion. Includes production-ready RAG patterns with Cloudflare Vectorize integration, chunking strategies, and caching patterns. Token savings: ~60%. Production tested. Keywords: gemini embeddings, gemini-embedding-001, google embeddings, semantic search, RAG, vector search, document clustering, similarity search, retrieval augmented generation, vectorize integration, cloudflare vectorize embeddings, 768 dimensions, embed content gemini, batch embeddings, embeddings api, cosine similarity, vector normalization, retrieval query, retrieval document, task types, dimension mismatch, embeddings rate limit, text truncation, @google/genai
zustand-state-management
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Production-tested setup for Zustand state management in React applications with TypeScript. This skill provides comprehensive patterns for building scalable, type-safe global state. Use when: setting up global state in React, migrating from Redux or Context API, implementing state persistence with localStorage, configuring TypeScript with Zustand, using slices pattern for modular stores, adding devtools middleware for debugging, handling Next.js SSR hydration, or encountering hydration errors, TypeScript inference issues, or persist middleware problems. Prevents 5 documented issues: Next.js hydration mismatches, TypeScript double parentheses syntax errors, persist middleware export errors, infinite render loops, and slices pattern type inference failures. Keywords: zustand, state management, React state, TypeScript state, persist middleware, devtools, slices pattern, global state, React hooks, create store, useBoundStore, StateCreator, hydration error, text content mismatch, infinite render, localStorage, sessionStorage, immer middleware, shallow equality, selector pattern, zustand v5
Systematic Debugging
mrgoonie
Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.
Root Cause Tracing
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Systematically trace bugs backward through call stack to find original trigger
Scale Game
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Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Verification Before Completion
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Run verification commands and confirm output before claiming success
Defense-in-Depth Validation
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Validate at every layer data passes through to make bugs impossible