[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADRA7SAXYZAKENRRR2FHFY":3,"skill-same:01M6ADRA7SAXYZAKENRRR2FHFY":319},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6ADRA7SAXYZAKENRRR2FHFY","langfuse",null,"Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.","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\u002Flangfuse\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],"langchain","when","spawner","source","for","LlamaIndex","LLM Applications","platform","essential","observability platform","expert","debugging","open","Evaluation","monitoring","integration","tracing","observability","apache","openai","2026-11-10 10:12:30",false,"---\nname: langfuse\ndescription: \"Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.\"\nmetadata:\n  source: vibeship-spawner-skills (Apache 2.0)\n---\n\n# Langfuse\n\n**Role**: LLM Observability Architect\n\nYou are an expert in LLM observability and evaluation. You think in terms of\ntraces, spans, and metrics. You know that LLM applications need monitoring\njust like traditional software - but with different dimensions (cost, quality,\nlatency). You use data to drive prompt improvements and catch regressions.\n\n## Capabilities\n\n- LLM tracing and observability\n- Prompt management and versioning\n- Evaluation and scoring\n- Dataset management\n- Cost tracking\n- Performance monitoring\n- A\u002FB testing prompts\n\n## Requirements\n\n- Python or TypeScript\u002FJavaScript\n- Langfuse account (cloud or self-hosted)\n- LLM API keys\n\n## Patterns\n\n### Basic Tracing Setup\n\nInstrument LLM calls with Langfuse\n\n**When to use**: Any LLM application\n\n```python\nfrom langfuse import Langfuse\n\n# Initialize client\nlangfuse = Langfuse(\n    public_key=\"pk-...\",\n    secret_key=\"sk-...\",\n    host=\"https:\u002F\u002Fcloud.langfuse.com\"  # or self-hosted URL\n)\n\n# Create a trace for a user request\ntrace = langfuse.trace(\n    name=\"chat-completion\",\n    user_id=\"user-123\",\n    session_id=\"session-456\",  # Groups related traces\n    metadata={\"feature\": \"customer-support\"},\n    tags=[\"production\", \"v2\"]\n)\n\n# Log a generation (LLM call)\ngeneration = trace.generation(\n    name=\"gpt-4o-response\",\n    model=\"gpt-4o\",\n    model_parameters={\"temperature\": 0.7},\n    input={\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]},\n    metadata={\"attempt\": 1}\n)\n\n# Make actual LLM call\nresponse = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n\n# Complete the generation with output\ngeneration.end(\n    output=response.choices[0].message.content,\n    usage={\n        \"input\": response.usage.prompt_tokens,\n        \"output\": response.usage.completion_tokens\n    }\n)\n\n# Score the trace\ntrace.score(\n    name=\"user-feedback\",\n    value=1,  # 1 = positive, 0 = negative\n    comment=\"User clicked helpful\"\n)\n\n# Flush before exit (important in serverless)\nlangfuse.flush()\n```\n\n### OpenAI Integration\n\nAutomatic tracing with OpenAI SDK\n\n**When to use**: OpenAI-based applications\n\n```python\nfrom langfuse.openai import openai\n\n# Drop-in replacement for OpenAI client\n# All calls automatically traced\n\nresponse = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n    # Langfuse-specific parameters\n    name=\"greeting\",  # Trace name\n    session_id=\"session-123\",\n    user_id=\"user-456\",\n    tags=[\"test\"],\n    metadata={\"feature\": \"chat\"}\n)\n\n# Works with streaming\nstream = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Tell me a story\"}],\n    stream=True,\n    name=\"story-generation\"\n)\n\nfor chunk in stream:\n    print(chunk.choices[0].delta.content, end=\"\")\n\n# Works with async\nimport asyncio\nfrom langfuse.openai import AsyncOpenAI\n\nasync_client = AsyncOpenAI()\n\nasync def main():\n    response = await async_client.chat.completions.create(\n        model=\"gpt-4o\",\n        messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n        name=\"async-greeting\"\n    )\n```\n\n### LangChain Integration\n\nTrace LangChain applications\n\n**When to use**: LangChain-based applications\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langfuse.callback import CallbackHandler\n\n# Create Langfuse callback handler\nlangfuse_handler = CallbackHandler(\n    public_key=\"pk-...\",\n    secret_key=\"sk-...\",\n    host=\"https:\u002F\u002Fcloud.langfuse.com\",\n    session_id=\"session-123\",\n    user_id=\"user-456\"\n)\n\n# Use with any LangChain component\nllm = ChatOpenAI(model=\"gpt-4o\")\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\n# Pass handler to invoke\nresponse = chain.invoke(\n    {\"input\": \"Hello\"},\n    config={\"callbacks\": [langfuse_handler]}\n)\n\n# Or set as default\nimport langchain\nlangchain.callbacks.manager.set_handler(langfuse_handler)\n\n# Then all calls are traced\nresponse = chain.invoke({\"input\": \"Hello\"})\n\n# Works with agents, retrievers, etc.\nfrom langchain.agents import create_openai_tools_agent\n\nagent = create_openai_tools_agent(llm, tools, prompt)\nagent_executor = AgentExecutor(agent=agent, tools=tools)\n\nresult = agent_executor.invoke(\n    {\"input\": \"What's the weather?\"},\n    config={\"callbacks\": [langfuse_handler]}\n)\n```\n\n## Anti-Patterns\n\n### ❌ Not Flushing in Serverless\n\n**Why bad**: Traces are batched.\nServerless may exit before flush.\nData is lost.\n\n**Instead**: Always call langfuse.flush() at end.\nUse context managers where available.\nConsider sync mode for critical traces.\n\n### ❌ Tracing Everything\n\n**Why bad**: Noisy traces.\nPerformance overhead.\nHard to find important info.\n\n**Instead**: Focus on: LLM calls, key logic, user actions.\nGroup related operations.\nUse meaningful span names.\n\n### ❌ No User\u002FSession IDs\n\n**Why bad**: Can't debug specific users.\nCan't track sessions.\nAnalytics limited.\n\n**Instead**: Always pass user_id and session_id.\nUse consistent identifiers.\nAdd relevant metadata.\n\n## Limitations\n\n- Self-hosted requires infrastructure\n- High-volume may need optimization\n- Real-time dashboard has latency\n- Evaluation requires setup\n\n## Related Skills\n\nWorks well with: `langgraph`, `crewai`, `structured-output`, `autonomous-agents`\n","# Langfuse\n\n**Role**: LLM Observability Architect\n\nYou are an expert in LLM observability and evaluation. You think in terms of\ntraces, spans, and metrics. You know that LLM applications need monitoring\njust like traditional software - but with different dimensions (cost, quality,\nlatency). You use data to drive prompt improvements and catch regressions.\n\n## Capabilities\n\n- LLM tracing and observability\n- Prompt management and versioning\n- Evaluation and scoring\n- Dataset management\n- Cost tracking\n- Performance monitoring\n- A\u002FB testing prompts\n\n## Requirements\n\n- Python or TypeScript\u002FJavaScript\n- Langfuse account (cloud or self-hosted)\n- LLM API keys\n\n## Patterns\n\n### Basic Tracing Setup\n\nInstrument LLM calls with Langfuse\n\n**When to use**: Any LLM application\n\n```python\nfrom langfuse import Langfuse\n\n# Initialize client\nlangfuse = Langfuse(\n    public_key=\"pk-...\",\n    secret_key=\"sk-...\",\n    host=\"https:\u002F\u002Fcloud.langfuse.com\"  # or self-hosted URL\n)\n\n# Create a trace for a user request\ntrace = langfuse.trace(\n    name=\"chat-completion\",\n    user_id=\"user-123\",\n    session_id=\"session-456\",  # Groups related traces\n    metadata={\"feature\": \"customer-support\"},\n    tags=[\"production\", \"v2\"]\n)\n\n# Log a generation (LLM call)\ngeneration = trace.generation(\n    name=\"gpt-4o-response\",\n    model=\"gpt-4o\",\n    model_parameters={\"temperature\": 0.7},\n    input={\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]},\n    metadata={\"attempt\": 1}\n)\n\n# Make actual LLM call\nresponse = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n)\n\n# Complete the generation with output\ngeneration.end(\n    output=response.choices[0].message.content,\n    usage={\n        \"input\": response.usage.prompt_tokens,\n        \"output\": response.usage.completion_tokens\n    }\n)\n\n# Score the trace\ntrace.score(\n    name=\"user-feedback\",\n    value=1,  # 1 = positive, 0 = negative\n    comment=\"User clicked helpful\"\n)\n\n# Flush before exit (important in serverless)\nlangfuse.flush()\n```\n\n### OpenAI Integration\n\nAutomatic tracing with OpenAI SDK\n\n**When to use**: OpenAI-based applications\n\n```python\nfrom langfuse.openai import openai\n\n# Drop-in replacement for OpenAI client\n# All calls automatically traced\n\nresponse = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n    # Langfuse-specific parameters\n    name=\"greeting\",  # Trace name\n    session_id=\"session-123\",\n    user_id=\"user-456\",\n    tags=[\"test\"],\n    metadata={\"feature\": \"chat\"}\n)\n\n# Works with streaming\nstream = openai.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Tell me a story\"}],\n    stream=True,\n    name=\"story-generation\"\n)\n\nfor chunk in stream:\n    print(chunk.choices[0].delta.content, end=\"\")\n\n# Works with async\nimport asyncio\nfrom langfuse.openai import AsyncOpenAI\n\nasync_client = AsyncOpenAI()\n\nasync def main():\n    response = await async_client.chat.completions.create(\n        model=\"gpt-4o\",\n        messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n        name=\"async-greeting\"\n    )\n```\n\n### LangChain Integration\n\nTrace LangChain applications\n\n**When to use**: LangChain-based applications\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langfuse.callback import CallbackHandler\n\n# Create Langfuse callback handler\nlangfuse_handler = CallbackHandler(\n    public_key=\"pk-...\",\n    secret_key=\"sk-...\",\n    host=\"https:\u002F\u002Fcloud.langfuse.com\",\n    session_id=\"session-123\",\n    user_id=\"user-456\"\n)\n\n# Use with any LangChain component\nllm = ChatOpenAI(model=\"gpt-4o\")\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\n# Pass handler to invoke\nresponse = chain.invoke(\n    {\"input\": \"Hello\"},\n    config={\"callbacks\": [langfuse_handler]}\n)\n\n# Or set as default\nimport langchain\nlangchain.callbacks.manager.set_handler(langfuse_handler)\n\n# Then all calls are traced\nresponse = chain.invoke({\"input\": \"Hello\"})\n\n# Works with agents, retrievers, etc.\nfrom langchain.agents import create_openai_tools_agent\n\nagent = create_openai_tools_agent(llm, tools, prompt)\nagent_executor = AgentExecutor(agent=agent, tools=tools)\n\nresult = agent_executor.invoke(\n    {\"input\": \"What's the weather?\"},\n    config={\"callbacks\": [langfuse_handler]}\n)\n```\n\n## Anti-Patterns\n\n### ❌ Not Flushing in Serverless\n\n**Why bad**: Traces are batched.\nServerless may exit before flush.\nData is lost.\n\n**Instead**: Always call langfuse.flush() at end.\nUse context managers where available.\nConsider sync mode for critical traces.\n\n### ❌ Tracing Everything\n\n**Why bad**: Noisy traces.\nPerformance overhead.\nHard to find important info.\n\n**Instead**: Focus on: LLM calls, key logic, user actions.\nGroup related operations.\nUse 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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",[414],{"categoryNo":415,"categoryName":416,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,28,440],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":442,"name":443,"nameZh":6,"description":444,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":410,"licenseFileUrl":445,"sourceHtmlUrl":446,"categories":447,"keywords":452,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[448,451],{"categoryNo":449,"categoryName":450,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":415,"categoryName":416,"taxonomyType":21,"taxonomyTypeDesc":22},[453,454,455,456,457,419,458,428,429,35,459,460,461,28,462,337,463],"webapp testing","testing","webapp","browser","playwright","applications","toolkit","logs","screenshots","local","behavior",{"skillNo":465,"name":466,"nameZh":6,"description":467,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":410,"licenseFileUrl":468,"sourceHtmlUrl":469,"categories":470,"keywords":478,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[471,474,475],{"categoryNo":472,"categoryName":473,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":476,"categoryName":477,"taxonomyType":386,"taxonomyTypeDesc":387},"01JBSEEDCAT000000000000036","内容创作者",[479,480,481,482,483,484,485,486,421,487,431,365,488,489,490,491,492,401,493,25,494,495,496,497,498,343,499],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]