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Found 132 Skills
Implementation guide for 17+ agentic AI architectures using LangChain and LangGraph for building sophisticated AI agents
Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
Skill for working with the Lucid Agents SDK - a TypeScript framework for building and monetizing AI agents. Use this skill when building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication. Activate when: Building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication, developing in the lucid-agents monorepo, creating new templates or CLI features, or questions about the Lucid Agents architecture or API.
Comprehensive guide for building full-stack applications with Convex and TanStack Start. This skill should be used when working on projects that use Convex as the backend database with TanStack Start (React meta-framework). Covers schema design, queries, mutations, actions, authentication with Better Auth, routing, data fetching patterns, SSR, file storage, scheduling, AI agents, and frontend patterns. Use this when implementing features, debugging issues, or needing guidance on Convex + TanStack Start best practices.
Build AI agents on Cloudflare Workers with MCP integration, tool use, and LLM providers.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Apply production-ready LangChain SDK patterns for chains, agents, and memory. Use when implementing LangChain integrations, refactoring code, or establishing team coding standards for LangChain applications. Trigger with phrases like "langchain SDK patterns", "langchain best practices", "langchain code patterns", "idiomatic langchain", "langchain architecture".
Build serverless TypeScript functions on Zavu Cloud — declare agents + tools in code with defineAgent / defineTool, deploy with `zavu deploy`, debug with `zavu agents executions`. Use this skill whenever the user wants code-driven AI agents, custom tool handlers, or event-driven business logic.
CLI and skills for building, evaluating, and deploying AI agents on Google Cloud's Gemini Enterprise Agent Platform using ADK
Structured learning roadmap for AI Agent development from LLM basics to multi-agent systems (bilingual Chinese/English)
Bootstrap a fresh Ubuntu VPS into a complete multi-agent AI development environment with safety tools and coordination infrastructure in 30 minutes
Drives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. "fix this issue in my agent", "this answer is wrong", "the agent is hallucinating", "improve my agent based on this trace", "make the agent do X instead of Y", "I want the agent to lead with/prioritize/recommend X", "new business rule: the agent should X", "always/never do X", "change the agent's default behavior" — or shares a trace they want addressed.