[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADRCQXQE1BGCGJCKEDN3KW":3,"skill-same:01M6ADRCQXQE1BGCGJCKEDN3KW":77},{"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":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6ADRCQXQE1BGCGJCKEDN3KW","langgraph",null,"Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Flanggraph\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"linkedin","when","spawner","stateful","for","with","building agents","multi","react agent","branches","expert","use","react","loop","human-in-the-loop","applications","persistence","graph","apache","framework","state","state management","2026-11-10 10:12:30",false,"---\nname: langgraph\ndescription: \"Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.\"\nmetadata:\n  source: vibeship-spawner-skills (Apache 2.0)\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","# 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",{"name":5,"metadata":51,"description":7},{"source":52},"vibeship-spawner-skills (Apache 2.0)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Flanggraph",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add davila7\u002Fclaude-code-templates --skill langgraph",{"tool":69,"command":70},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill langgraph",{"tool":72,"command":73},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill langgraph","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Flanggraph\u002FSKILL.md",true,[],[78,102,129,162,194,217],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":85,"publishedAt":46},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[84],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[86,87,88,89,90,28,91,92,35,25,93,29,94,95,96,97,98,99,100,101],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":109,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[108],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[110,111,112,113,114,115,116,117,118,119,120,25,121,122,123,124,125,126,127,128],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":144,"publishedAt":46},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[135,138,139],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[145,146,147,148,149,150,151,28,152,153,154,155,156,157,158,35,159,160,25,161],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":46},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[174,175,176,177,178,44,179,45,180,181,182,183,184,185,186,187,36,188,189,190,191,192,28,193],"artifacts builder","web","web artifacts builder","artifacts","builder","routing","component","css","frontend","ui","Tools","file","components","shadcn","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":205,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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