[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H7GZJS2FQ0MZDB70VQJQFD":3,"skill-same:01M6H7GZJS2FQ0MZDB70VQJQFD":69},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6H7GZJS2FQ0MZDB70VQJQFD","langchain-webhooks-events",null,"Implement LangChain callback and event handling for webhooks.\nUse when integrating with external systems, implementing streaming,\nor building event-driven LangChain applications.\nTrigger with phrases like \"langchain callbacks\", \"langchain webhooks\",\n\"langchain events\", \"langchain streaming\", \"callback handler\".","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-webhooks-events\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"langchain webhooks events","langchain","webhooks","events","io","for","implementing","callback","with","event","event-driven","trigger","codex","use","write","read","applications","streaming","code","implement","2026-11-10 10:12:30",false,"---\nname: langchain-webhooks-events\ndescription: |\n  Implement LangChain callback and event handling for webhooks.\n  Use when integrating with external systems, implementing streaming,\n  or building event-driven LangChain applications.\n  Trigger with phrases like \"langchain callbacks\", \"langchain webhooks\",\n  \"langchain events\", \"langchain streaming\", \"callback handler\".\nallowed-tools: Read, Write, Edit\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ncompatible-with: claude-code, codex, openclaw\n---\n# LangChain Webhooks & Events\n\n## Overview\nImplement callback handlers and event-driven patterns for LangChain applications including streaming, webhooks, and real-time updates.\n\n## Prerequisites\n- LangChain application configured\n- Understanding of async programming\n- Webhook endpoint (for external integrations)\n\n## Instructions\n\n### Step 1: Create Custom Callback Handler\n```python\nfrom langchain_core.callbacks import BaseCallbackHandler\nfrom langchain_core.messages import BaseMessage\nfrom typing import Any, Dict, List\nimport httpx\n\nclass WebhookCallbackHandler(BaseCallbackHandler):\n    \"\"\"Send events to external webhook.\"\"\"\n\n    def __init__(self, webhook_url: str):\n        self.webhook_url = webhook_url\n        self.client = httpx.Client(timeout=10.0)\n\n    def on_llm_start(\n        self,\n        serialized: Dict[str, Any],\n        prompts: List[str],\n        **kwargs\n    ) -> None:\n        \"\"\"Called when LLM starts.\"\"\"\n        self._send_event(\"llm_start\", {\n            \"model\": serialized.get(\"name\"),\n            \"prompt_count\": len(prompts)\n        })\n\n    def on_llm_end(self, response, **kwargs) -> None:\n        \"\"\"Called when LLM completes.\"\"\"\n        self._send_event(\"llm_end\", {\n            \"generations\": len(response.generations),\n            \"token_usage\": response.llm_output.get(\"token_usage\") if response.llm_output else None\n        })\n\n    def on_llm_error(self, error: Exception, **kwargs) -> None:\n        \"\"\"Called on LLM error.\"\"\"\n        self._send_event(\"llm_error\", {\n            \"error_type\": type(error).__name__,\n            \"message\": str(error)\n        })\n\n    def on_chain_start(\n        self,\n        serialized: Dict[str, Any],\n        inputs: Dict[str, Any],\n        **kwargs\n    ) -> None:\n        \"\"\"Called when chain starts.\"\"\"\n        self._send_event(\"chain_start\", {\n            \"chain\": serialized.get(\"name\"),\n            \"input_keys\": list(inputs.keys())\n        })\n\n    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n        \"\"\"Called when chain completes.\"\"\"\n        self._send_event(\"chain_end\", {\n            \"output_keys\": list(outputs.keys())\n        })\n\n    def on_tool_start(\n        self,\n        serialized: Dict[str, Any],\n        input_str: str,\n        **kwargs\n    ) -> None:\n        \"\"\"Called when tool starts.\"\"\"\n        self._send_event(\"tool_start\", {\n            \"tool\": serialized.get(\"name\"),\n            \"input_length\": len(input_str)\n        })\n\n    def _send_event(self, event_type: str, data: Dict[str, Any]) -> None:\n        \"\"\"Send event to webhook.\"\"\"\n        try:\n            self.client.post(self.webhook_url, json={\n                \"event\": event_type,\n                \"data\": data,\n                \"timestamp\": datetime.now().isoformat()\n            })\n        except Exception as e:\n            print(f\"Webhook error: {e}\")\n```\n\n### Step 2: Implement Streaming Handler\n```python\nfrom langchain_core.callbacks import StreamingStdOutCallbackHandler\nimport asyncio\nfrom typing import AsyncIterator\n\nclass StreamingWebSocketHandler(BaseCallbackHandler):\n    \"\"\"Stream tokens to WebSocket clients.\"\"\"\n\n    def __init__(self, websocket):\n        self.websocket = websocket\n        self.queue = asyncio.Queue()\n\n    async def on_llm_new_token(self, token: str, **kwargs) -> None:\n        \"\"\"Called for each new token.\"\"\"\n        await self.queue.put(token)\n\n    async def on_llm_end(self, response, **kwargs) -> None:\n        \"\"\"Signal end of stream.\"\"\"\n        await self.queue.put(None)\n\n    async def stream_tokens(self) -> AsyncIterator[str]:\n        \"\"\"Iterate over streamed tokens.\"\"\"\n        while True:\n            token = await self.queue.get()\n            if token is None:\n                break\n            yield token\n\n# FastAPI WebSocket endpoint\nfrom fastapi import WebSocket\n\n@app.websocket(\"\u002Fws\u002Fchat\")\nasync def websocket_chat(websocket: WebSocket):\n    await websocket.accept()\n\n    handler = StreamingWebSocketHandler(websocket)\n    llm = ChatOpenAI(streaming=True, callbacks=[handler])\n\n    while True:\n        data = await websocket.receive_json()\n\n        # Start streaming in background\n        asyncio.create_task(chain.ainvoke(\n            {\"input\": data[\"message\"]},\n            config={\"callbacks\": [handler]}\n        ))\n\n        # Stream tokens to client\n        async for token in handler.stream_tokens():\n            await websocket.send_json({\"token\": token})\n```\n\n### Step 3: Server-Sent Events (SSE)\n```python\nfrom fastapi import Request\nfrom fastapi.responses import StreamingResponse\nfrom langchain_openai import ChatOpenAI\n\n@app.get(\"\u002Fchat\u002Fstream\")\nasync def stream_chat(request: Request, message: str):\n    \"\"\"Stream response using Server-Sent Events.\"\"\"\n\n    async def event_generator():\n        llm = ChatOpenAI(model=\"gpt-4o-mini\", streaming=True)\n        prompt = ChatPromptTemplate.from_template(\"{input}\")\n        chain = prompt | llm\n\n        async for chunk in chain.astream({\"input\": message}):\n            if await request.is_disconnected():\n                break\n            yield f\"data: {chunk.content}\\n\\n\"\n\n        yield \"data: [DONE]\\n\\n\"\n\n    return StreamingResponse(\n        event_generator(),\n        media_type=\"text\u002Fevent-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"Connection\": \"keep-alive\",\n        }\n    )\n```\n\n### Step 4: Event Aggregation\n```python\nfrom dataclasses import dataclass, field\nfrom datetime import datetime\nfrom typing import List\n\n@dataclass\nclass ChainEvent:\n    event_type: str\n    timestamp: datetime\n    data: Dict[str, Any]\n\n@dataclass\nclass ChainTrace:\n    chain_id: str\n    events: List[ChainEvent] = field(default_factory=list)\n    start_time: datetime = None\n    end_time: datetime = None\n\nclass TraceAggregator(BaseCallbackHandler):\n    \"\"\"Aggregate all events for a chain execution.\"\"\"\n\n    def __init__(self):\n        self.traces: Dict[str, ChainTrace] = {}\n\n    def on_chain_start(self, serialized, inputs, run_id, **kwargs):\n        self.traces[str(run_id)] = ChainTrace(\n            chain_id=str(run_id),\n            start_time=datetime.now()\n        )\n        self._add_event(run_id, \"chain_start\", {\"inputs\": inputs})\n\n    def on_chain_end(self, outputs, run_id, **kwargs):\n        self._add_event(run_id, \"chain_end\", {\"outputs\": outputs})\n        if str(run_id) in self.traces:\n            self.traces[str(run_id)].end_time = datetime.now()\n\n    def _add_event(self, run_id, event_type, data):\n        trace = self.traces.get(str(run_id))\n        if trace:\n            trace.events.append(ChainEvent(\n                event_type=event_type,\n                timestamp=datetime.now(),\n                data=data\n            ))\n\n    def get_trace(self, run_id: str) -> ChainTrace:\n        return self.traces.get(run_id)\n```\n\n## Output\n- Custom webhook callback handler\n- WebSocket streaming implementation\n- Server-Sent Events endpoint\n- Event aggregation for tracing\n\n## Examples\n\n### Using Callbacks\n```python\nfrom langchain_openai import ChatOpenAI\n\nwebhook_handler = WebhookCallbackHandler(\"https:\u002F\u002Fapi.example.com\u002Fwebhook\")\n\nllm = ChatOpenAI(\n    model=\"gpt-4o-mini\",\n    callbacks=[webhook_handler]\n)\n\n# All LLM calls will trigger webhook events\nresponse = llm.invoke(\"Hello!\")\n```\n\n### Client-Side SSE Consumption\n```javascript\n\u002F\u002F JavaScript client\nconst eventSource = new EventSource('\u002Fchat\u002Fstream?message=Hello');\n\neventSource.onmessage = (event) => {\n    if (event.data === '[DONE]') {\n        eventSource.close();\n        return;\n    }\n    document.getElementById('output').textContent += event.data;\n};\n```\n\n## Error Handling\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Webhook Timeout | Slow endpoint | Increase timeout, use async |\n| WebSocket Disconnect | Client closed | Handle disconnect gracefully |\n| Event Queue Full | Too many events | Add queue size limit |\n| SSE Timeout | Long response | Add keep-alive pings |\n\n## Resources\n- [LangChain Callbacks](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Fcallbacks\u002F)\n- [FastAPI WebSocket](https:\u002F\u002Ffastapi.tiangolo.com\u002Fadvanced\u002Fwebsockets\u002F)\n- [Server-Sent Events](https:\u002F\u002Fdeveloper.mozilla.org\u002Fen-US\u002Fdocs\u002FWeb\u002FAPI\u002FServer-sent_events)\n\n## Next Steps\nUse `langchain-observability` for comprehensive monitoring.","# LangChain Webhooks & Events\n\n## Overview\nImplement callback handlers and event-driven patterns for LangChain applications including streaming, webhooks, and real-time updates.\n\n## Prerequisites\n- LangChain application configured\n- Understanding of async programming\n- Webhook endpoint (for external integrations)\n\n## Instructions\n\n### Step 1: Create Custom Callback Handler\n```python\nfrom langchain_core.callbacks import BaseCallbackHandler\nfrom langchain_core.messages import BaseMessage\nfrom typing import Any, Dict, List\nimport httpx\n\nclass WebhookCallbackHandler(BaseCallbackHandler):\n    \"\"\"Send events to external webhook.\"\"\"\n\n    def __init__(self, webhook_url: str):\n        self.webhook_url = webhook_url\n        self.client = httpx.Client(timeout=10.0)\n\n    def on_llm_start(\n        self,\n        serialized: Dict[str, Any],\n        prompts: List[str],\n        **kwargs\n    ) -> None:\n        \"\"\"Called when LLM starts.\"\"\"\n        self._send_event(\"llm_start\", {\n            \"model\": serialized.get(\"name\"),\n            \"prompt_count\": len(prompts)\n        })\n\n    def on_llm_end(self, response, **kwargs) -> None:\n        \"\"\"Called when LLM completes.\"\"\"\n        self._send_event(\"llm_end\", {\n            \"generations\": len(response.generations),\n            \"token_usage\": response.llm_output.get(\"token_usage\") if response.llm_output else None\n        })\n\n    def on_llm_error(self, error: Exception, **kwargs) -> None:\n        \"\"\"Called on LLM error.\"\"\"\n        self._send_event(\"llm_error\", {\n            \"error_type\": type(error).__name__,\n            \"message\": str(error)\n        })\n\n    def on_chain_start(\n        self,\n        serialized: Dict[str, Any],\n        inputs: Dict[str, Any],\n        **kwargs\n    ) -> None:\n        \"\"\"Called when chain starts.\"\"\"\n        self._send_event(\"chain_start\", {\n            \"chain\": serialized.get(\"name\"),\n            \"input_keys\": list(inputs.keys())\n        })\n\n    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n        \"\"\"Called when chain completes.\"\"\"\n        self._send_event(\"chain_end\", {\n            \"output_keys\": list(outputs.keys())\n        })\n\n    def on_tool_start(\n        self,\n        serialized: Dict[str, Any],\n        input_str: str,\n        **kwargs\n    ) -> None:\n        \"\"\"Called when tool starts.\"\"\"\n        self._send_event(\"tool_start\", {\n            \"tool\": serialized.get(\"name\"),\n            \"input_length\": len(input_str)\n        })\n\n    def _send_event(self, event_type: str, data: Dict[str, Any]) -> None:\n        \"\"\"Send event to webhook.\"\"\"\n        try:\n            self.client.post(self.webhook_url, json={\n                \"event\": event_type,\n                \"data\": data,\n                \"timestamp\": datetime.now().isoformat()\n            })\n        except Exception as e:\n            print(f\"Webhook error: {e}\")\n```\n\n### Step 2: Implement Streaming Handler\n```python\nfrom langchain_core.callbacks import StreamingStdOutCallbackHandler\nimport asyncio\nfrom typing import AsyncIterator\n\nclass StreamingWebSocketHandler(BaseCallbackHandler):\n    \"\"\"Stream tokens to WebSocket clients.\"\"\"\n\n    def __init__(self, websocket):\n        self.websocket = websocket\n        self.queue = asyncio.Queue()\n\n    async def on_llm_new_token(self, token: str, **kwargs) -> None:\n        \"\"\"Called for each new token.\"\"\"\n        await self.queue.put(token)\n\n    async def on_llm_end(self, response, **kwargs) -> None:\n        \"\"\"Signal end of stream.\"\"\"\n        await self.queue.put(None)\n\n    async def stream_tokens(self) -> AsyncIterator[str]:\n        \"\"\"Iterate over streamed tokens.\"\"\"\n        while True:\n            token = await self.queue.get()\n            if token is None:\n                break\n            yield token\n\n# FastAPI WebSocket endpoint\nfrom fastapi import WebSocket\n\n@app.websocket(\"\u002Fws\u002Fchat\")\nasync def websocket_chat(websocket: WebSocket):\n    await websocket.accept()\n\n    handler = StreamingWebSocketHandler(websocket)\n    llm = ChatOpenAI(streaming=True, callbacks=[handler])\n\n    while True:\n        data = await websocket.receive_json()\n\n        # Start streaming in background\n        asyncio.create_task(chain.ainvoke(\n            {\"input\": data[\"message\"]},\n            config={\"callbacks\": [handler]}\n        ))\n\n        # Stream tokens to client\n        async for token in handler.stream_tokens():\n            await websocket.send_json({\"token\": token})\n```\n\n### Step 3: Server-Sent Events (SSE)\n```python\nfrom fastapi import Request\nfrom fastapi.responses import StreamingResponse\nfrom langchain_openai import ChatOpenAI\n\n@app.get(\"\u002Fchat\u002Fstream\")\nasync def stream_chat(request: Request, message: str):\n    \"\"\"Stream response using Server-Sent Events.\"\"\"\n\n    async def event_generator():\n        llm = ChatOpenAI(model=\"gpt-4o-mini\", streaming=True)\n        prompt = ChatPromptTemplate.from_template(\"{input}\")\n        chain = prompt | llm\n\n        async for chunk in chain.astream({\"input\": message}):\n            if await request.is_disconnected():\n                break\n            yield f\"data: {chunk.content}\\n\\n\"\n\n        yield \"data: [DONE]\\n\\n\"\n\n    return StreamingResponse(\n        event_generator(),\n        media_type=\"text\u002Fevent-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"Connection\": \"keep-alive\",\n        }\n    )\n```\n\n### Step 4: Event Aggregation\n```python\nfrom dataclasses import dataclass, field\nfrom datetime import datetime\nfrom typing import List\n\n@dataclass\nclass ChainEvent:\n    event_type: str\n    timestamp: datetime\n    data: Dict[str, Any]\n\n@dataclass\nclass ChainTrace:\n    chain_id: str\n    events: List[ChainEvent] = field(default_factory=list)\n    start_time: datetime = None\n    end_time: datetime = None\n\nclass TraceAggregator(BaseCallbackHandler):\n    \"\"\"Aggregate all events for a chain execution.\"\"\"\n\n    def __init__(self):\n        self.traces: Dict[str, ChainTrace] = {}\n\n    def on_chain_start(self, serialized, inputs, run_id, **kwargs):\n        self.traces[str(run_id)] = ChainTrace(\n            chain_id=str(run_id),\n            start_time=datetime.now()\n        )\n        self._add_event(run_id, \"chain_start\", {\"inputs\": inputs})\n\n    def on_chain_end(self, outputs, run_id, **kwargs):\n        self._add_event(run_id, \"chain_end\", {\"outputs\": outputs})\n        if str(run_id) in self.traces:\n            self.traces[str(run_id)].end_time = datetime.now()\n\n    def _add_event(self, run_id, event_type, data):\n        trace = self.traces.get(str(run_id))\n        if trace:\n            trace.events.append(ChainEvent(\n                event_type=event_type,\n                timestamp=datetime.now(),\n                data=data\n            ))\n\n    def get_trace(self, run_id: str) -> ChainTrace:\n        return self.traces.get(run_id)\n```\n\n## Output\n- Custom webhook callback handler\n- WebSocket streaming implementation\n- Server-Sent Events endpoint\n- Event aggregation for tracing\n\n## Examples\n\n### Using Callbacks\n```python\nfrom langchain_openai import ChatOpenAI\n\nwebhook_handler = WebhookCallbackHandler(\"https:\u002F\u002Fapi.example.com\u002Fwebhook\")\n\nllm = ChatOpenAI(\n    model=\"gpt-4o-mini\",\n    callbacks=[webhook_handler]\n)\n\n# All LLM calls will trigger webhook events\nresponse = llm.invoke(\"Hello!\")\n```\n\n### Client-Side SSE Consumption\n```javascript\n\u002F\u002F JavaScript client\nconst eventSource = new EventSource('\u002Fchat\u002Fstream?message=Hello');\n\neventSource.onmessage = (event) => {\n    if (event.data === '[DONE]') {\n        eventSource.close();\n        return;\n    }\n    document.getElementById('output').textContent += event.data;\n};\n```\n\n## Error Handling\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Webhook Timeout | Slow endpoint | Increase timeout, use async |\n| WebSocket Disconnect | Client closed | Handle disconnect gracefully |\n| Event Queue Full | Too many events | Add queue size limit |\n| SSE Timeout | Long response | Add keep-alive pings |\n\n## Resources\n- [LangChain Callbacks](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Fcallbacks\u002F)\n- [FastAPI WebSocket](https:\u002F\u002Ffastapi.tiangolo.com\u002Fadvanced\u002Fwebsockets\u002F)\n- [Server-Sent Events](https:\u002F\u002Fdeveloper.mozilla.org\u002Fen-US\u002Fdocs\u002FWeb\u002FAPI\u002FServer-sent_events)\n\n## Next Steps\nUse `langchain-observability` for comprehensive monitoring.",{"name":5,"author":43,"license":14,"version":44,"description":45,"allowed-tools":46,"compatible-with":47},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Implement LangChain callback and event handling for webhooks.\nUse when integrating with external systems, implementing streaming,\nor building event-driven LangChain applications.\nTrigger with phrases like \"langchain callbacks\", \"langchain webhooks\",\n\"langchain events\", \"langchain streaming\", \"callback handler\".\n","Read, Write, Edit","claude-code, codex, 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Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fazure-apim-architecture\u002FSKILL.md",[76,81],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,23],"architecture","azure","azure apim architecture","component","authentication","gateway","topology","Optimization","network","api","team","environment","multi-environment","workspaces","instances","selection","strategies","azure api management","mode",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":120,"publishedAt":38},"01M6HBT7J4H082JEEA670B3EQ9","apiops-deployment","Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep\u002FTerraform), CI\u002FCD pipelines (GitHub Actions\u002FAzure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapiops-deployment\u002FSKILL.md",[112,113,116,119],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},{"categoryNo":114,"categoryName":115,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":117,"categoryName":118,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},[121,122,123,124,88,125,36,126,127,128,129,130,96,97,103,131,132,104,133,24],"deployment","apiops deployment","workflows","azure devops","infrastructure","infrastructure as code","actions","ci","github actions","github-actions","as","guides","CI\u002FCD",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"publishedAt":38},"01M6HBT2HEA23THNE9E00GYQBE","apim-policy-authoring","Creates production-ready Azure API Management policy XML for authentication (OAuth 2.0, JWT validation, subscription keys), rate limiting, CORS configuration, error handling, and API transformations. Use when implementing API security, access control, or request\u002Fresponse processing logic.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapim-policy-authoring\u002FSKILL.md",[140],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},[142,143,144,145,88,91,96,97,146,31,147,148,149,150,151,104,152,153,24,23],"policy","authoring","apim policy authoring","security","access","ready","api security","Access Control","error","processing","cors configuration","production-ready",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":162,"publishedAt":38},"01M6HBSY65SB29J9WE9KYD0K6T","api-security-review","Reviews Azure API Management configurations for security vulnerabilities, OWASP API Security Top 10 compliance, VNet Internal mode validation, Private Link verification, and Azure Security Benchmark alignment. Use when performing security audits, pre-deployment validation, or compliance reviews.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapi-security-review\u002FSKILL.md",[160,161],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},[163,145,164,96,148,165,88,121,166,167,168,169,97,170,104,171,172,173,105,23],"review","security review","api security review","benchmark","verification","owasp","owasp-api","alignment","azure security","reviews","top",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":180,"publishedAt":38},"01M6HBSRW4PDFE6K4F8JYPZQ05","jira-cli","Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fjira-cli\u002FSKILL.md",[],[181,182,183,123,184,185,186,187,96,188,189,190,191,192,193,194,195,196,197,198],"cli","jira","jira cli","init","tasks","create","issues","common","work","command","edit","list","view","project","project work","token","line","sprints",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":205,"publishedAt":38},"01M6HBSNS3JHMW603G47TWE31H","calculator","Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fcalculator\u002FSKILL.md",[],[201,206,207,208,209,210,211,31,212,213],"npm","js","calculate","node","math","user","compute","errors"]