Skill: gechengling/agent-skills-framework-explorer (version 1.0.0)

--- name: "Agent Skills Framework Explorer" description: "AI-powered assistant for exploring, understanding, and building with AI agent skills frameworks — covers Anthropic agent-skills, OpenAI agent SDKs, LangChain tools, CrewAI protocols, and open-source agent skill marketplaces. Built for AI developers and agent builders. Keywords: agent-skills, AI agent framework, LangChain tools, CrewAI, OpenAI Agents SDK, Claude Agent SDK, agent tool ecosystem, skills marketplace, MCP tools, n8n agent nodes, agent protocol." version: "1.0.0" --- # Agent Skills Framework Explorer ## Overview A comprehensive guide and assistant for navigating the rapidly evolving AI agent skills framework ecosystem. Whether you're exploring addyosmani/agent-skills (40K+ stars), building with Anthropic's Claude Agent SDK, designing multi-agent pipelines with CrewAI/LangGraph, or connecting tools via MCP protocol — this skill helps you understand, compare, and implement the right framework for your use case. ## Triggers - "compare agent frameworks" - "how to use agent-skills" - "build a multi-agent pipeline with [framework]" - "what is MCP protocol" - "CrewAI vs LangGraph vs OpenAI Agents SDK" - "find tools for my agent" - "agent framework comparison" - "MCP server setup" - "AI Agent工具框架对比" - "多智能体框架选型" ## Workflow ### Step 1: Identify the User's Goal Determine the primary use case: - **Framework selection**: Comparing options for a new project - **Tool integration**: Adding capabilities to an existing agent - **Multi-agent design**: Orchestrating multiple specialized agents - **Skills marketplace**: Finding pre-built agent capabilities - **Protocol understanding**: Learning MCP, Agent Protocol, or other standards - **Migration**: Moving from one framework to another ### Step 2: Framework Deep Dive Provide structured comparison and guidance for the relevant framework: #### Anthropic agent-skills (addyosmani/agent-skills) - **What it is**: Shell-based skill framework for CLI agents, Claude Code, and GitHub Actions - **Key concepts**: `skill.yaml` metadata, bash/python/shell execution, skill chaining - **Use when**: Building CLI tools, automation scripts, developer-facing agents - **Ecosystem**: 40K+ stars, active community, skill registry at agent-skills.dev - **Example skill structure**: ``` skill.yaml (name, triggers, description, tools) run.sh (main execution script) references/ (docs, examples) ``` #### OpenAI Agents SDK - **What it is**: Official Python SDK for building multi-agent systems - **Key concepts**: Handoffs, tracing, guardrails, function calling - **Use when**: Building customer-facing agents on OpenAI models - **Best for**: Product teams needing production-ready agents with built-in observability #### Claude Agent SDK (Anthropic) - **What it is**: SDK for building Claude-powered agents with tool use - **Key concepts**: Tools, sessions, context management, computer use - **Use when**: Deep Claude integration, computer-use agents, complex tool chains #### LangGraph (LangChain) - **What it is**: Graph-based framework for cyclical agent workflows - **Key concepts**: Nodes, edges, state machines, human-in-the-loop - **Use when**: Complex workflows with branching, loops, and multi-agent coordination #### CrewAI - **What it is**: Role-based multi-agent framework - **Key concepts**: Agents with roles/goals/backstory, task delegation, crew orchestration - **Use when**: Team-based AI workflows (e.g., research crew, writing crew) - **Best for**: Business users who want multi-agent without deep coding #### MCP (Model Context Protocol) - **What it is**: Open protocol for connecting AI models to external tools - **Key concepts**: Servers, clients, resources, prompts, tools - **Ecosystem**: 50+ official and community servers (GitHub, Slack, Postgres, etc.) - **Use when**: Connecting agents to real-world data and services - **Reference**: https://modelcontextprotocol.io ### Step 3: Hands-On Implementation For each framework, provide starter code and best practices: #### Setting up agent-skills ```bash # Clone the framework git clone https://github.com/addyosmani/agent-skills # Register a new skill cd agent-skills/skills npx create-skill my-skill # skill.yaml example name: my-skill triggers: - "run my task" - "execute my workflow" description: "A custom agent skill" tools: - bash - filesystem execute: ./run.sh ``` #### Building with CrewAI ```python from crewai import Agent, Task, Crew researcher = Agent( role="Financial Analyst", goal="Research insurance market trends", backstory="Expert in insurance data analysis", tools=[...], ) research_task = Task( description="Analyze Q1 2026 insurance market trends", agent=researcher, ) crew = Crew(agents=[researcher], tasks=[research_task]) result = crew.kickoff() ``` #### Connecting via MCP ```python from mcp.client import MCPClient client = MCPClient() # Connect to GitHub MCP server await client.connect("github", [REDACTED_SECRET]"GITHUB_TOKEN")) # Use tools from the server result = await client.call_tool("github", "create_issue", { "repo": "owner/repo", "title": "Agent-generated task", }) ``` ### Step 4: Framework Comparison Matrix | Criteria | agent-skills | OpenAI Agents SDK | Claude Agent SDK | LangGraph | CrewAI | MCP | |----------|-------------|-------------------|-----------------|-----------|--------|-----| | Multi-agent | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | | Ease of use | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | | Production-ready | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | | Customization | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | | Tool ecosystem | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | | Best for | Dev tools, CLI | Product agents | Claude-native | Complex workflows | Business teams | Tool integration | ### Step 5: Skill Discovery Help users find pre-built skills for their needs: 1. Search agent-skills.dev registry by category 2. Find MCP servers for specific tools (database, API, etc.) 3. Discover CrewAI templates and Crew templates 4. Identify gaps where a custom skill should be built ## Input/Output Examples ### Example 1: Framework Selection **Input:** ``` I want to build an AI agent that researches stock trends and writes a weekly report. Which framework should I use? ``` **Output:** ``` Recommendation: CrewAI + MCP combination Rationale: - Research + writing = multi-role workflow → CrewAI's agent delegation shines - Stock data requires external API tools → MCP for data connections - Weekly automation → agent-skills for scheduling Suggested stack: 1. CrewAI crew: Researcher agent (MCP-connected) + Writer agent 2. MCP servers: Financial data API, web search 3. agent-skills: Weekly trigger, report delivery Implementation path: Week 1: Set up CrewAI with 2 agents Week 2: Add MCP financial data tools Week 3: Deploy via agent-skills scheduler ``` ### Example 2: MCP Server Selection **Input:** ``` I need to connect my agent to PostgreSQL and Slack. Which MCP servers should I use? ``` **Output:** ``` Recommended MCP Servers: 1. PostgreSQL MCP Server - npm install @modelcontextprotocol/server-postgres - Capabilities: query, schema inspection, backup - Config: DATABASE_URL env var 2. Slack MCP Server - npm install @modelcontextprotocol/server-slack - Capabilities: send messages, read channels, manage threads - Config: SLACK_BOT_TOKEN, SLACK_TEAM_ID Quick setup: ```bash npx mcp install @modelcontextprotocol/server-postgres npx mcp install @modelcontextprotocol/server-slack ``` Security note: Store tokens in environment variables, never hardcode. ``` ## Key Frameworks Reference | Framework | Repository | Stars | Primary Language | License | |-----------|-----------|-------|-----------------|---------| | agent-skills | addyosmani/agent-skills | 40K+ | Shell | MIT | | OpenAI Agents SDK | openai/openai-agents-python | Growing | Python | Apache 2.0 | | Claude Agent SDK | anthropics/anthropic-cookbook | - | Python | - | | LangGraph | langchain-ai/langgraph | - | Python | MIT | | CrewAI | crewAI同事/crewai | - | Python | MIT | | MCP | modelcontextprotocol/spec | - | Multi | Apache 2.0 | ## Best Practices 1. **Start with the right abstraction level** — agent-skills for CLI tools, CrewAI for business workflows, LangGraph for complex state machines 2. **Use MCP for all external integrations** — it provides a standardized, swappable interface 3. **Combine frameworks** — use CrewAI for orchestration + MCP for tools + agent-skills for deployment 4. **Monitor agent behavior** — all major frameworks support tracing (OpenAI, LangSmith, etc.) 5. **Design for failure** — agents can hallucinate; add guardrails and human-in-the-loop for critical actions


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