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Found 2,500 Skills
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
Pull Google Search Console data and perform search performance analysis. Use when asked about search rankings, clicks, impressions, CTR, index coverage, Core Web Vitals, or sitemap status. Trigger phrases: "search console", "GSC", "search performance", "clicks and impressions", "CTR analysis", "index coverage", "core web vitals", "URL inspection", "sitemap status", "ranking data", "search queries", "keyword positions".
Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use PROACTIVELY for any Cloudflare development task.
Supports commands: [commit|commit_a commit_b] [--verify] [--apply] [--commit] Interactive git diff review skill. Parses git diff output into individual hunks, presents each hunk to the user with analysis for accept/reject decisions, verifies complete coverage, and generates a Markdown review report.
Comprehensive Cloudflare platform knowledge covering Workers, storage (R2/D1/KV/Durable Objects/Queues), AI Workers, Hyperdrive, Zero Trust, MCP servers, Workflows, and all platform features
Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.
Tavily AI search API integration via curl. Use this skill to perform live web search and RAG-style retrieval.
Build AI agents with persistent threads, tool calling, and streaming on Convex. Use when implementing chat interfaces, AI assistants, multi-agent workflows, RAG systems, or any LLM-powered features with message history.
Use when writing, fixing, editing, or refactoring Python tests. Enforces Clean Code principles—fast tests, boundary coverage, one assert per test.
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.
Systematic approach to implementing new features in the Rust memory system following project conventions. Use when adding new functionality with proper testing and documentation, maintaining code quality and test coverage.