[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M713W8MNQPSAB153FS01SS3M":3,"skill-same:01M713W8MNQPSAB153FS01SS3M":65},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M713W8MNQPSAB153FS01SS3M","langgraph",null,"You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Flanggraph\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"structure","2026","spawner","unknown","for","you","with","understand","make","graphs","design","expert","use","Production","flow","persistence","apache","state","ai agents","2026-11-10 10:12:30",false,"---\nname: langgraph\ndescription: \"You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.\"\nrisk: unknown\nsource: \"vibeship-spawner-skills (Apache 2.0)\"\ndate_added: \"2026-02-27\"\n---\n\n# LangGraph\n\n**Role**: LangGraph Agent Architect\n\nYou are an expert in building production-grade AI agents with LangGraph. You\nunderstand that agents need explicit structure - graphs make the flow visible\nand debuggable. You design state carefully, use reducers appropriately, and\nalways consider persistence for production. You know when cycles are needed\nand how to prevent infinite loops.\n\n## Capabilities\n\n- Graph construction (StateGraph)\n- State management and reducers\n- Node and edge definitions\n- Conditional routing\n- Checkpointers and persistence\n- Human-in-the-loop patterns\n- Tool integration\n- Streaming and async execution\n\n## Requirements\n\n- Python 3.9+\n- langgraph package\n- LLM API access (OpenAI, Anthropic, etc.)\n- Understanding of graph concepts\n\n## Patterns\n\n### Basic Agent Graph\n\nSimple ReAct-style agent with tools\n\n**When to use**: Single agent with tool calling\n\n```python\nfrom typing import Annotated, TypedDict\nfrom langgraph.graph import StateGraph, START, END\nfrom langgraph.graph.message import add_messages\nfrom langgraph.prebuilt import ToolNode\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.tools import tool\n\n# 1. Define State\nclass AgentState(TypedDict):\n    messages: Annotated[list, add_messages]\n    # add_messages reducer appends, doesn't overwrite\n\n# 2. Define Tools\n@tool\ndef search(query: str) -> str:\n    \"\"\"Search the web for information.\"\"\"\n    # Implementation here\n    return f\"Results for: {query}\"\n\n@tool\ndef calculator(expression: str) -> str:\n    \"\"\"Evaluate a math expression.\"\"\"\n    return str(eval(expression))\n\ntools = [search, calculator]\n\n# 3. Create LLM with tools\nllm = ChatOpenAI(model=\"gpt-4o\").bind_tools(tools)\n\n# 4. Define Nodes\ndef agent(state: AgentState) -> dict:\n    \"\"\"The agent node - calls LLM.\"\"\"\n    response = llm.invoke(state[\"messages\"])\n    return {\"messages\": [response]}\n\n# Tool node handles tool execution\ntool_node = ToolNode(tools)\n\n# 5. Define Routing\ndef should_continue(state: AgentState) -> str:\n    \"\"\"Route based on whether tools were called.\"\"\"\n    last_message = state[\"messages\"][-1]\n    if last_message.tool_calls:\n        return \"tools\"\n    return END\n\n# 6. Build Graph\ngraph = StateGraph(AgentState)\n\n# Add nodes\ngraph.add_node(\"agent\", agent)\ngraph.add_node(\"tools\", tool_node)\n\n# Add edges\ngraph.add_edge(START, \"agent\")\ngraph.add_conditional_edges(\"agent\", should_continue, [\"tools\", END])\ngraph.add_edge(\"tools\", \"agent\")  # Loop back\n\n# Compile\napp = graph.compile()\n\n# 7. Run\nresult = app.invoke({\n    \"messages\": [(\"user\", \"What is 25 * 4?\")]\n})\n```\n\n### State with Reducers\n\nComplex state management with custom reducers\n\n**When to use**: Multiple agents updating shared state\n\n```python\nfrom typing import Annotated, TypedDict\nfrom operator import add\nfrom langgraph.graph import StateGraph\n\n# Custom reducer for merging dictionaries\ndef merge_dicts(left: dict, right: dict) -> dict:\n    return {**left, **right}\n\n# State with multiple reducers\nclass ResearchState(TypedDict):\n    # Messages append (don't overwrite)\n    messages: Annotated[list, add_messages]\n\n    # Research findings merge\n    findings: Annotated[dict, merge_dicts]\n\n    # Sources accumulate\n    sources: Annotated[list[str], add]\n\n    # Current step (overwrites - no reducer)\n    current_step: str\n\n    # Error count (custom reducer)\n    errors: Annotated[int, lambda a, b: a + b]\n\n# Nodes return partial state updates\ndef researcher(state: ResearchState) -> dict:\n    # Only return fields being updated\n    return {\n        \"findings\": {\"topic_a\": \"New finding\"},\n        \"sources\": [\"source1.com\"],\n        \"current_step\": \"researching\"\n    }\n\ndef writer(state: ResearchState) -> dict:\n    # Access accumulated state\n    all_findings = state[\"findings\"]\n    all_sources = state[\"sources\"]\n\n    return {\n        \"messages\": [(\"assistant\", f\"Report based on {len(all_sources)} sources\")],\n        \"current_step\": \"writing\"\n    }\n\n# Build graph\ngraph = StateGraph(ResearchState)\ngraph.add_node(\"researcher\", researcher)\ngraph.add_node(\"writer\", writer)\n# ... add edges\n```\n\n### Conditional Branching\n\nRoute to different paths based on state\n\n**When to use**: Multiple possible workflows\n\n```python\nfrom langgraph.graph import StateGraph, START, END\n\nclass RouterState(TypedDict):\n    query: str\n    query_type: str\n    result: str\n\ndef classifier(state: RouterState) -> dict:\n    \"\"\"Classify the query type.\"\"\"\n    query = state[\"query\"].lower()\n    if \"code\" in query or \"program\" in query:\n        return {\"query_type\": \"coding\"}\n    elif \"search\" in query or \"find\" in query:\n        return {\"query_type\": \"search\"}\n    else:\n        return {\"query_type\": \"chat\"}\n\ndef coding_agent(state: RouterState) -> dict:\n    return {\"result\": \"Here's your code...\"}\n\ndef search_agent(state: RouterState) -> dict:\n    return {\"result\": \"Search results...\"}\n\ndef chat_agent(state: RouterState) -> dict:\n    return {\"result\": \"Let me help...\"}\n\n# Routing function\ndef route_query(state: RouterState) -> str:\n    \"\"\"Route to appropriate agent.\"\"\"\n    query_type = state[\"query_type\"]\n    return query_type  # Returns node name\n\n# Build graph\ngraph = StateGraph(RouterState)\n\ngraph.add_node(\"classifier\", classifier)\ngraph.add_node(\"coding\", coding_agent)\ngraph.add_node(\"search\", search_agent)\ngraph.add_node(\"chat\", chat_agent)\n\ngraph.add_edge(START, \"classifier\")\n\n# Conditional edges from classifier\ngraph.add_conditional_edges(\n    \"classifier\",\n    route_query,\n    {\n        \"coding\": \"coding\",\n        \"search\": \"search\",\n        \"chat\": \"chat\"\n    }\n)\n\n# All agents lead to END\ngraph.add_edge(\"coding\", END)\ngraph.add_edge(\"search\", END)\ngraph.add_edge(\"chat\", END)\n\napp = graph.compile()\n```\n\n## Anti-Patterns\n\n### ❌ Infinite Loop Without Exit\n\n**Why bad**: Agent loops forever.\nBurns tokens and costs.\nEventually errors out.\n\n**Instead**: Always have exit conditions:\n- Max iterations counter in state\n- Clear END conditions in routing\n- Timeout at application level\n\ndef should_continue(state):\n    if state[\"iterations\"] > 10:\n        return END\n    if state[\"task_complete\"]:\n        return END\n    return \"agent\"\n\n### ❌ Stateless Nodes\n\n**Why bad**: Loses LangGraph's benefits.\nState not persisted.\nCan't resume conversations.\n\n**Instead**: Always use state for data flow.\nReturn state updates from nodes.\nUse reducers for accumulation.\nLet LangGraph manage state.\n\n### ❌ Giant Monolithic State\n\n**Why bad**: Hard to reason about.\nUnnecessary data in context.\nSerialization overhead.\n\n**Instead**: Use input\u002Foutput schemas for clean interfaces.\nPrivate state for internal data.\nClear separation of concerns.\n\n## Limitations\n\n- Python-only (TypeScript in early stages)\n- Learning curve for graph concepts\n- State management complexity\n- Debugging can be challenging\n\n## Related Skills\n\nWorks well with: `crewai`, `autonomous-agents`, `langfuse`, `structured-output`\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# LangGraph\n\n**Role**: LangGraph Agent Architect\n\nYou are an expert in building production-grade AI agents with LangGraph. You\nunderstand that agents need explicit structure - graphs make the flow visible\nand debuggable. You design state carefully, use reducers appropriately, and\nalways consider persistence for production. You know when cycles are needed\nand how to prevent infinite loops.\n\n## Capabilities\n\n- Graph construction (StateGraph)\n- State management and reducers\n- Node and edge definitions\n- Conditional routing\n- Checkpointers and persistence\n- Human-in-the-loop patterns\n- Tool integration\n- Streaming and async execution\n\n## Requirements\n\n- Python 3.9+\n- langgraph package\n- LLM API access (OpenAI, Anthropic, etc.)\n- Understanding of graph concepts\n\n## Patterns\n\n### Basic Agent Graph\n\nSimple ReAct-style agent with tools\n\n**When to use**: Single agent with tool calling\n\n```python\nfrom typing import Annotated, TypedDict\nfrom langgraph.graph import StateGraph, START, END\nfrom langgraph.graph.message import add_messages\nfrom langgraph.prebuilt import ToolNode\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.tools import tool\n\n# 1. Define State\nclass AgentState(TypedDict):\n    messages: Annotated[list, add_messages]\n    # add_messages reducer appends, doesn't overwrite\n\n# 2. Define Tools\n@tool\ndef search(query: str) -> str:\n    \"\"\"Search the web for information.\"\"\"\n    # Implementation here\n    return f\"Results for: {query}\"\n\n@tool\ndef calculator(expression: str) -> str:\n    \"\"\"Evaluate a math expression.\"\"\"\n    return str(eval(expression))\n\ntools = [search, calculator]\n\n# 3. Create LLM with tools\nllm = ChatOpenAI(model=\"gpt-4o\").bind_tools(tools)\n\n# 4. Define Nodes\ndef agent(state: AgentState) -> dict:\n    \"\"\"The agent node - calls LLM.\"\"\"\n    response = llm.invoke(state[\"messages\"])\n    return {\"messages\": [response]}\n\n# Tool node handles tool execution\ntool_node = ToolNode(tools)\n\n# 5. Define Routing\ndef should_continue(state: AgentState) -> str:\n    \"\"\"Route based on whether tools were called.\"\"\"\n    last_message = state[\"messages\"][-1]\n    if last_message.tool_calls:\n        return \"tools\"\n    return END\n\n# 6. Build Graph\ngraph = StateGraph(AgentState)\n\n# Add nodes\ngraph.add_node(\"agent\", agent)\ngraph.add_node(\"tools\", tool_node)\n\n# Add edges\ngraph.add_edge(START, \"agent\")\ngraph.add_conditional_edges(\"agent\", should_continue, [\"tools\", END])\ngraph.add_edge(\"tools\", \"agent\")  # Loop back\n\n# Compile\napp = graph.compile()\n\n# 7. Run\nresult = app.invoke({\n    \"messages\": [(\"user\", \"What is 25 * 4?\")]\n})\n```\n\n### State with Reducers\n\nComplex state management with custom reducers\n\n**When to use**: Multiple agents updating shared state\n\n```python\nfrom typing import Annotated, TypedDict\nfrom operator import add\nfrom langgraph.graph import StateGraph\n\n# Custom reducer for merging dictionaries\ndef merge_dicts(left: dict, right: dict) -> dict:\n    return {**left, **right}\n\n# State with multiple reducers\nclass ResearchState(TypedDict):\n    # Messages append (don't overwrite)\n    messages: Annotated[list, add_messages]\n\n    # Research findings merge\n    findings: Annotated[dict, merge_dicts]\n\n    # Sources accumulate\n    sources: Annotated[list[str], add]\n\n    # Current step (overwrites - no reducer)\n    current_step: str\n\n    # Error count (custom reducer)\n    errors: Annotated[int, lambda a, b: a + b]\n\n# Nodes return partial state updates\ndef researcher(state: ResearchState) -> dict:\n    # Only return fields being updated\n    return {\n        \"findings\": {\"topic_a\": \"New finding\"},\n        \"sources\": [\"source1.com\"],\n        \"current_step\": \"researching\"\n    }\n\ndef writer(state: ResearchState) -> dict:\n    # Access accumulated state\n    all_findings = state[\"findings\"]\n    all_sources = state[\"sources\"]\n\n    return {\n        \"messages\": [(\"assistant\", f\"Report based on {len(all_sources)} sources\")],\n        \"current_step\": \"writing\"\n    }\n\n# Build graph\ngraph = StateGraph(ResearchState)\ngraph.add_node(\"researcher\", researcher)\ngraph.add_node(\"writer\", writer)\n# ... add edges\n```\n\n### Conditional Branching\n\nRoute to different paths based on state\n\n**When to use**: Multiple possible workflows\n\n```python\nfrom langgraph.graph import StateGraph, START, END\n\nclass RouterState(TypedDict):\n    query: str\n    query_type: str\n    result: str\n\ndef classifier(state: RouterState) -> dict:\n    \"\"\"Classify the query type.\"\"\"\n    query = state[\"query\"].lower()\n    if \"code\" in query or \"program\" in query:\n        return {\"query_type\": \"coding\"}\n    elif \"search\" in query or \"find\" in query:\n        return {\"query_type\": \"search\"}\n    else:\n        return {\"query_type\": \"chat\"}\n\ndef coding_agent(state: RouterState) -> dict:\n    return {\"result\": \"Here's your code...\"}\n\ndef search_agent(state: RouterState) -> dict:\n    return {\"result\": \"Search results...\"}\n\ndef chat_agent(state: RouterState) -> dict:\n    return {\"result\": \"Let me help...\"}\n\n# Routing function\ndef route_query(state: RouterState) -> str:\n    \"\"\"Route to appropriate agent.\"\"\"\n    query_type = state[\"query_type\"]\n    return query_type  # Returns node name\n\n# Build graph\ngraph = StateGraph(RouterState)\n\ngraph.add_node(\"classifier\", classifier)\ngraph.add_node(\"coding\", coding_agent)\ngraph.add_node(\"search\", search_agent)\ngraph.add_node(\"chat\", chat_agent)\n\ngraph.add_edge(START, \"classifier\")\n\n# Conditional edges from classifier\ngraph.add_conditional_edges(\n    \"classifier\",\n    route_query,\n    {\n        \"coding\": \"coding\",\n        \"search\": \"search\",\n        \"chat\": \"chat\"\n    }\n)\n\n# All agents lead to END\ngraph.add_edge(\"coding\", END)\ngraph.add_edge(\"search\", END)\ngraph.add_edge(\"chat\", END)\n\napp = graph.compile()\n```\n\n## Anti-Patterns\n\n### ❌ Infinite Loop Without Exit\n\n**Why bad**: Agent loops forever.\nBurns tokens and costs.\nEventually errors out.\n\n**Instead**: Always have exit conditions:\n- Max iterations counter in state\n- Clear END conditions in routing\n- Timeout at application level\n\ndef should_continue(state):\n    if state[\"iterations\"] > 10:\n        return END\n    if state[\"task_complete\"]:\n        return END\n    return \"agent\"\n\n### ❌ Stateless Nodes\n\n**Why bad**: Loses LangGraph's benefits.\nState not persisted.\nCan't resume conversations.\n\n**Instead**: Always use state for data flow.\nReturn state updates from nodes.\nUse reducers for accumulation.\nLet LangGraph manage state.\n\n### ❌ Giant Monolithic State\n\n**Why bad**: Hard to reason about.\nUnnecessary data in context.\nSerialization overhead.\n\n**Instead**: Use input\u002Foutput schemas for clean interfaces.\nPrivate state for internal data.\nClear separation of concerns.\n\n## Limitations\n\n- Python-only (TypeScript in early stages)\n- Learning curve for graph concepts\n- State management complexity\n- Debugging can be challenging\n\n## Related Skills\n\nWorks well with: `crewai`, `autonomous-agents`, `langfuse`, `structured-output`\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":22,"source":43,"date_added":44,"description":7},"vibeship-spawner-skills (Apache 2.0)","2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fcopilot\u002Flanggraph","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill langgraph",{"tool":57,"command":58},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill langgraph",{"tool":60,"command":61},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill langgraph","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Flanggraph\u002FSKILL.md",true,[],[66,88,114,124,137,156],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":77,"publishedAt":38},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[78,79,80,81,82,29,83,84,85,86,87,25],"wechat search","wechat","search","capabilities","openclaw","web search","compliance","web","official","fetch",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":38},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). 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