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Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.","zhengliangliahu-cloud\u002Fskills","zhengliangliahu-cloud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F248268067?v=4","01M6NAQ109P2Y11XSV5D9QJFFY","Lin","01M6NAQ15R1M4E18CA2KPJHC06",1,"MIT","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F17-observability\u002Flangsmith\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"observability","langsmith observability","LangSmith","use","for","orchestra","LLM Applications","systematic","platform","observability platform","datasets","testing","ops","model","applications","Evaluation","monitoring","tracing","debugging","research","2026-11-10 10:12:30",false,"---\nname: langsmith-observability\ndescription: LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Observability, LangSmith, Tracing, Evaluation, Monitoring, Debugging, Testing, LLM Ops, Production]\ndependencies: [langsmith>=0.2.0]\n---\n\n# LangSmith - LLM Observability Platform\n\nDevelopment platform for debugging, evaluating, and monitoring language models and AI applications.\n\n## When to use LangSmith\n\n**Use LangSmith when:**\n- Debugging LLM application issues (prompts, chains, agents)\n- Evaluating model outputs systematically against datasets\n- Monitoring production LLM systems\n- Building regression testing for AI features\n- Analyzing latency, token usage, and costs\n- Collaborating on prompt engineering\n\n**Key features:**\n- **Tracing**: Capture inputs, outputs, latency for all LLM calls\n- **Evaluation**: Systematic testing with built-in and custom evaluators\n- **Datasets**: Create test sets from production traces or manually\n- **Monitoring**: Track metrics, errors, and costs in production\n- **Integrations**: Works with OpenAI, Anthropic, LangChain, LlamaIndex\n\n**Use alternatives instead:**\n- **Weights & Biases**: Deep learning experiment tracking, model training\n- **MLflow**: General ML lifecycle, model registry focus\n- **Arize\u002FWhyLabs**: ML monitoring, data drift detection\n\n## Quick start\n\n### Installation\n\n```bash\npip install langsmith\n\n# Set environment variables\nexport LANGSMITH_API_KEY=\"your-api-key\"\nexport LANGSMITH_TRACING=true\n```\n\n### Basic tracing with @traceable\n\n```python\nfrom langsmith import traceable\nfrom openai import OpenAI\n\nclient = OpenAI()\n\n@traceable\ndef generate_response(prompt: str) -> str:\n    response = client.chat.completions.create(\n        model=\"gpt-4o\",\n        messages=[{\"role\": \"user\", \"content\": prompt}]\n    )\n    return response.choices[0].message.content\n\n# Automatically traced to LangSmith\nresult = generate_response(\"What is machine learning?\")\n```\n\n### OpenAI wrapper (automatic tracing)\n\n```python\nfrom langsmith.wrappers import wrap_openai\nfrom openai import OpenAI\n\n# Wrap client for automatic tracing\nclient = wrap_openai(OpenAI())\n\n# All calls automatically traced\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\n```\n\n## Core concepts\n\n### Runs and traces\n\nA **run** is a single execution unit (LLM call, chain, tool). Runs form hierarchical **traces** showing the full execution flow.\n\n```python\nfrom langsmith import traceable\n\n@traceable(run_type=\"chain\")\ndef process_query(query: str) -> str:\n    # Parent run\n    context = retrieve_context(query)  # Child run\n    response = generate_answer(query, context)  # Child run\n    return response\n\n@traceable(run_type=\"retriever\")\ndef retrieve_context(query: str) -> list:\n    return vector_store.search(query)\n\n@traceable(run_type=\"llm\")\ndef generate_answer(query: str, context: list) -> str:\n    return llm.invoke(f\"Context: {context}\\n\\nQuestion: {query}\")\n```\n\n### Projects\n\nProjects organize related runs. Set via environment or code:\n\n```python\nimport os\nos.environ[\"LANGSMITH_PROJECT\"] = \"my-project\"\n\n# Or per-function\n@traceable(project_name=\"my-project\")\ndef my_function():\n    pass\n```\n\n## Client API\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# List runs\nruns = list(client.list_runs(\n    project_name=\"my-project\",\n    filter='eq(status, \"success\")',\n    limit=100\n))\n\n# Get run details\nrun = client.read_run(run_id=\"...\")\n\n# Create feedback\nclient.create_feedback(\n    run_id=\"...\",\n    key=\"correctness\",\n    score=0.9,\n    comment=\"Good answer\"\n)\n```\n\n## Datasets and evaluation\n\n### Create dataset\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Create dataset\ndataset = client.create_dataset(\"qa-test-set\", description=\"QA evaluation\")\n\n# Add examples\nclient.create_examples(\n    inputs=[\n        {\"question\": \"What is Python?\"},\n        {\"question\": \"What is ML?\"}\n    ],\n    outputs=[\n        {\"answer\": \"A programming language\"},\n        {\"answer\": \"Machine learning\"}\n    ],\n    dataset_id=dataset.id\n)\n```\n\n### Run evaluation\n\n```python\nfrom langsmith import evaluate\n\ndef my_model(inputs: dict) -> dict:\n    # Your model logic\n    return {\"answer\": generate_answer(inputs[\"question\"])}\n\ndef correctness_evaluator(run, example):\n    prediction = run.outputs[\"answer\"]\n    reference = example.outputs[\"answer\"]\n    score = 1.0 if reference.lower() in prediction.lower() else 0.0\n    return {\"key\": \"correctness\", \"score\": score}\n\nresults = evaluate(\n    my_model,\n    data=\"qa-test-set\",\n    evaluators=[correctness_evaluator],\n    experiment_prefix=\"v1\"\n)\n\nprint(f\"Average score: {results.aggregate_metrics['correctness']}\")\n```\n\n### Built-in evaluators\n\n```python\nfrom langsmith.evaluation import LangChainStringEvaluator\n\n# Use LangChain evaluators\nresults = evaluate(\n    my_model,\n    data=\"qa-test-set\",\n    evaluators=[\n        LangChainStringEvaluator(\"qa\"),\n        LangChainStringEvaluator(\"cot_qa\")\n    ]\n)\n```\n\n## Advanced tracing\n\n### Tracing context\n\n```python\nfrom langsmith import tracing_context\n\nwith tracing_context(\n    project_name=\"experiment-1\",\n    tags=[\"production\", \"v2\"],\n    metadata={\"version\": \"2.0\"}\n):\n    # All traceable calls inherit context\n    result = my_function()\n```\n\n### Manual runs\n\n```python\nfrom langsmith import trace\n\nwith trace(\n    name=\"custom_operation\",\n    run_type=\"tool\",\n    inputs={\"query\": \"test\"}\n) as run:\n    result = do_something()\n    run.end(outputs={\"result\": result})\n```\n\n### Process inputs\u002Foutputs\n\n```python\ndef sanitize_inputs(inputs: dict) -> dict:\n    if \"password\" in inputs:\n        inputs[\"password\"] = \"***\"\n    return inputs\n\n@traceable(process_inputs=sanitize_inputs)\ndef login(username: str, password: str):\n    return authenticate(username, password)\n```\n\n### Sampling\n\n```python\nimport os\nos.environ[\"LANGSMITH_TRACING_SAMPLING_RATE\"] = \"0.1\"  # 10% sampling\n```\n\n## LangChain integration\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\n\n# Tracing enabled automatically with LANGSMITH_TRACING=true\nllm = ChatOpenAI(model=\"gpt-4o\")\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\n# All chain runs traced automatically\nresponse = chain.invoke({\"input\": \"Hello!\"})\n```\n\n## Production monitoring\n\n### Hub prompts\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Pull prompt from hub\nprompt = client.pull_prompt(\"my-org\u002Fqa-prompt\")\n\n# Use in application\nresult = prompt.invoke({\"question\": \"What is AI?\"})\n```\n\n### Async client\n\n```python\nfrom langsmith import AsyncClient\n\nasync def main():\n    client = AsyncClient()\n\n    runs = []\n    async for run in client.list_runs(project_name=\"my-project\"):\n        runs.append(run)\n\n    return runs\n```\n\n### Feedback collection\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Collect user feedback\ndef record_feedback(run_id: str, user_rating: int, comment: str = None):\n    client.create_feedback(\n        run_id=run_id,\n        key=\"user_rating\",\n        score=user_rating \u002F 5.0,  # Normalize to 0-1\n        comment=comment\n    )\n\n# In your application\nrecord_feedback(run_id=\"...\", user_rating=4, comment=\"Helpful response\")\n```\n\n## Testing integration\n\n### Pytest integration\n\n```python\nfrom langsmith import test\n\n@test\ndef test_qa_accuracy():\n    result = my_qa_function(\"What is Python?\")\n    assert \"programming\" in result.lower()\n```\n\n### Evaluation in CI\u002FCD\n\n```python\nfrom langsmith import evaluate\n\ndef run_evaluation():\n    results = evaluate(\n        my_model,\n        data=\"regression-test-set\",\n        evaluators=[accuracy_evaluator]\n    )\n\n    # Fail CI if accuracy drops\n    assert results.aggregate_metrics[\"accuracy\"] >= 0.9, \\\n        f\"Accuracy {results.aggregate_metrics['accuracy']} below threshold\"\n```\n\n## Best practices\n\n1. **Structured naming** - Use consistent project\u002Frun naming conventions\n2. **Add metadata** - Include version, environment, user info\n3. **Sample in production** - Use sampling rate to control volume\n4. **Create datasets** - Build test sets from interesting production cases\n5. **Automate evaluation** - Run evaluations in CI\u002FCD pipelines\n6. **Monitor costs** - Track token usage and latency trends\n\n## Common issues\n\n**Traces not appearing:**\n```python\nimport os\n# Ensure tracing is enabled\nos.environ[\"LANGSMITH_TRACING\"] = \"true\"\nos.environ[\"LANGSMITH_API_KEY\"] = \"your-key\"\n\n# Verify connection\nfrom langsmith import Client\nclient = Client()\nprint(client.list_projects())  # Should work\n```\n\n**High latency from tracing:**\n```python\n# Enable background batching (default)\nfrom langsmith import Client\nclient = Client(auto_batch_tracing=True)\n\n# Or use sampling\nos.environ[\"LANGSMITH_TRACING_SAMPLING_RATE\"] = \"0.1\"\n```\n\n**Large payloads:**\n```python\n# Hide sensitive\u002Flarge fields\n@traceable(\n    process_inputs=lambda x: {k: v for k, v in x.items() if k != \"large_field\"}\n)\ndef my_function(data):\n    pass\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom evaluators, distributed tracing, hub prompts\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.smith.langchain.com\n- **Python SDK**: https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk\n- **Web App**: https:\u002F\u002Fsmith.langchain.com\n- **Version**: 0.2.0+\n- **License**: MIT\n","# LangSmith - LLM Observability Platform\n\nDevelopment platform for debugging, evaluating, and monitoring language models and AI applications.\n\n## When to use LangSmith\n\n**Use LangSmith when:**\n- Debugging LLM application issues (prompts, chains, agents)\n- Evaluating model outputs systematically against datasets\n- Monitoring production LLM systems\n- Building regression testing for AI features\n- Analyzing latency, token usage, and costs\n- Collaborating on prompt engineering\n\n**Key features:**\n- **Tracing**: Capture inputs, outputs, latency for all LLM calls\n- **Evaluation**: Systematic testing with built-in and custom evaluators\n- **Datasets**: Create test sets from production traces or manually\n- **Monitoring**: Track metrics, errors, and costs in production\n- **Integrations**: Works with OpenAI, Anthropic, LangChain, LlamaIndex\n\n**Use alternatives instead:**\n- **Weights & Biases**: Deep learning experiment tracking, model training\n- **MLflow**: General ML lifecycle, model registry focus\n- **Arize\u002FWhyLabs**: ML monitoring, data drift detection\n\n## Quick start\n\n### Installation\n\n```bash\npip install langsmith\n\n# Set environment variables\nexport LANGSMITH_API_KEY=\"your-api-key\"\nexport LANGSMITH_TRACING=true\n```\n\n### Basic tracing with @traceable\n\n```python\nfrom langsmith import traceable\nfrom openai import OpenAI\n\nclient = OpenAI()\n\n@traceable\ndef generate_response(prompt: str) -> str:\n    response = client.chat.completions.create(\n        model=\"gpt-4o\",\n        messages=[{\"role\": \"user\", \"content\": prompt}]\n    )\n    return response.choices[0].message.content\n\n# Automatically traced to LangSmith\nresult = generate_response(\"What is machine learning?\")\n```\n\n### OpenAI wrapper (automatic tracing)\n\n```python\nfrom langsmith.wrappers import wrap_openai\nfrom openai import OpenAI\n\n# Wrap client for automatic tracing\nclient = wrap_openai(OpenAI())\n\n# All calls automatically traced\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}]\n)\n```\n\n## Core concepts\n\n### Runs and traces\n\nA **run** is a single execution unit (LLM call, chain, tool). Runs form hierarchical **traces** showing the full execution flow.\n\n```python\nfrom langsmith import traceable\n\n@traceable(run_type=\"chain\")\ndef process_query(query: str) -> str:\n    # Parent run\n    context = retrieve_context(query)  # Child run\n    response = generate_answer(query, context)  # Child run\n    return response\n\n@traceable(run_type=\"retriever\")\ndef retrieve_context(query: str) -> list:\n    return vector_store.search(query)\n\n@traceable(run_type=\"llm\")\ndef generate_answer(query: str, context: list) -> str:\n    return llm.invoke(f\"Context: {context}\\n\\nQuestion: {query}\")\n```\n\n### Projects\n\nProjects organize related runs. Set via environment or code:\n\n```python\nimport os\nos.environ[\"LANGSMITH_PROJECT\"] = \"my-project\"\n\n# Or per-function\n@traceable(project_name=\"my-project\")\ndef my_function():\n    pass\n```\n\n## Client API\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# List runs\nruns = list(client.list_runs(\n    project_name=\"my-project\",\n    filter='eq(status, \"success\")',\n    limit=100\n))\n\n# Get run details\nrun = client.read_run(run_id=\"...\")\n\n# Create feedback\nclient.create_feedback(\n    run_id=\"...\",\n    key=\"correctness\",\n    score=0.9,\n    comment=\"Good answer\"\n)\n```\n\n## Datasets and evaluation\n\n### Create dataset\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Create dataset\ndataset = client.create_dataset(\"qa-test-set\", description=\"QA evaluation\")\n\n# Add examples\nclient.create_examples(\n    inputs=[\n        {\"question\": \"What is Python?\"},\n        {\"question\": \"What is ML?\"}\n    ],\n    outputs=[\n        {\"answer\": \"A programming language\"},\n        {\"answer\": \"Machine learning\"}\n    ],\n    dataset_id=dataset.id\n)\n```\n\n### Run evaluation\n\n```python\nfrom langsmith import evaluate\n\ndef my_model(inputs: dict) -> dict:\n    # Your model logic\n    return {\"answer\": generate_answer(inputs[\"question\"])}\n\ndef correctness_evaluator(run, example):\n    prediction = run.outputs[\"answer\"]\n    reference = example.outputs[\"answer\"]\n    score = 1.0 if reference.lower() in prediction.lower() else 0.0\n    return {\"key\": \"correctness\", \"score\": score}\n\nresults = evaluate(\n    my_model,\n    data=\"qa-test-set\",\n    evaluators=[correctness_evaluator],\n    experiment_prefix=\"v1\"\n)\n\nprint(f\"Average score: {results.aggregate_metrics['correctness']}\")\n```\n\n### Built-in evaluators\n\n```python\nfrom langsmith.evaluation import LangChainStringEvaluator\n\n# Use LangChain evaluators\nresults = evaluate(\n    my_model,\n    data=\"qa-test-set\",\n    evaluators=[\n        LangChainStringEvaluator(\"qa\"),\n        LangChainStringEvaluator(\"cot_qa\")\n    ]\n)\n```\n\n## Advanced tracing\n\n### Tracing context\n\n```python\nfrom langsmith import tracing_context\n\nwith tracing_context(\n    project_name=\"experiment-1\",\n    tags=[\"production\", \"v2\"],\n    metadata={\"version\": \"2.0\"}\n):\n    # All traceable calls inherit context\n    result = my_function()\n```\n\n### Manual runs\n\n```python\nfrom langsmith import trace\n\nwith trace(\n    name=\"custom_operation\",\n    run_type=\"tool\",\n    inputs={\"query\": \"test\"}\n) as run:\n    result = do_something()\n    run.end(outputs={\"result\": result})\n```\n\n### Process inputs\u002Foutputs\n\n```python\ndef sanitize_inputs(inputs: dict) -> dict:\n    if \"password\" in inputs:\n        inputs[\"password\"] = \"***\"\n    return inputs\n\n@traceable(process_inputs=sanitize_inputs)\ndef login(username: str, password: str):\n    return authenticate(username, password)\n```\n\n### Sampling\n\n```python\nimport os\nos.environ[\"LANGSMITH_TRACING_SAMPLING_RATE\"] = \"0.1\"  # 10% sampling\n```\n\n## LangChain integration\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\n\n# Tracing enabled automatically with LANGSMITH_TRACING=true\nllm = ChatOpenAI(model=\"gpt-4o\")\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\n# All chain runs traced automatically\nresponse = chain.invoke({\"input\": \"Hello!\"})\n```\n\n## Production monitoring\n\n### Hub prompts\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Pull prompt from hub\nprompt = client.pull_prompt(\"my-org\u002Fqa-prompt\")\n\n# Use in application\nresult = prompt.invoke({\"question\": \"What is AI?\"})\n```\n\n### Async client\n\n```python\nfrom langsmith import AsyncClient\n\nasync def main():\n    client = AsyncClient()\n\n    runs = []\n    async for run in client.list_runs(project_name=\"my-project\"):\n        runs.append(run)\n\n    return runs\n```\n\n### Feedback collection\n\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Collect user feedback\ndef record_feedback(run_id: str, user_rating: int, comment: str = None):\n    client.create_feedback(\n        run_id=run_id,\n        key=\"user_rating\",\n        score=user_rating \u002F 5.0,  # Normalize to 0-1\n        comment=comment\n    )\n\n# In your application\nrecord_feedback(run_id=\"...\", user_rating=4, comment=\"Helpful response\")\n```\n\n## Testing integration\n\n### Pytest integration\n\n```python\nfrom langsmith import test\n\n@test\ndef test_qa_accuracy():\n    result = my_qa_function(\"What is Python?\")\n    assert \"programming\" in result.lower()\n```\n\n### Evaluation in CI\u002FCD\n\n```python\nfrom langsmith import evaluate\n\ndef run_evaluation():\n    results = evaluate(\n        my_model,\n        data=\"regression-test-set\",\n        evaluators=[accuracy_evaluator]\n    )\n\n    # Fail CI if accuracy drops\n    assert results.aggregate_metrics[\"accuracy\"] >= 0.9, \\\n        f\"Accuracy {results.aggregate_metrics['accuracy']} below threshold\"\n```\n\n## Best practices\n\n1. **Structured naming** - Use consistent project\u002Frun naming conventions\n2. **Add metadata** - Include version, environment, user info\n3. **Sample in production** - Use sampling rate to control volume\n4. **Create datasets** - Build test sets from interesting production cases\n5. **Automate evaluation** - Run evaluations in CI\u002FCD pipelines\n6. **Monitor costs** - Track token usage and latency trends\n\n## Common issues\n\n**Traces not appearing:**\n```python\nimport os\n# Ensure tracing is enabled\nos.environ[\"LANGSMITH_TRACING\"] = \"true\"\nos.environ[\"LANGSMITH_API_KEY\"] = \"your-key\"\n\n# Verify connection\nfrom langsmith import Client\nclient = Client()\nprint(client.list_projects())  # Should work\n```\n\n**High latency from tracing:**\n```python\n# Enable background batching (default)\nfrom langsmith import Client\nclient = Client(auto_batch_tracing=True)\n\n# Or use sampling\nos.environ[\"LANGSMITH_TRACING_SAMPLING_RATE\"] = \"0.1\"\n```\n\n**Large payloads:**\n```python\n# Hide sensitive\u002Flarge fields\n@traceable(\n    process_inputs=lambda x: {k: v for k, v in x.items() if k != \"large_field\"}\n)\ndef my_function(data):\n    pass\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom evaluators, distributed tracing, hub prompts\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.smith.langchain.com\n- **Python SDK**: https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk\n- **Web App**: https:\u002F\u002Fsmith.langchain.com\n- **Version**: 0.2.0+\n- **License**: MIT\n",{"name":5,"tags":49,"author":57,"license":15,"version":58,"description":7,"dependencies":59},[50,26,51,39,52,53,54,55,56],"Observability","Tracing","Monitoring","Debugging","Testing","LLM Ops","Production","Orchestra Research","1.0.0",[60],"langsmith>=0.2.0","main","75c7c92896e878938460f0bceb23b6ab4a787286","AI-Research-SKILLs\u002F17-observability\u002Flangsmith","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud","A postgraduate student majoring in mathematics.","2026-06-03 19:04:43",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add zhengliangliahu-cloud\u002Fskills --skill langsmith-observability",{"tool":74,"command":75},"bunx","bunx skills add zhengliangliahu-cloud\u002Fskills --skill langsmith-observability",{"tool":77,"command":78},"pnpm","pnpm dlx skills add zhengliangliahu-cloud\u002Fskills --skill langsmith-observability","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F17-observability\u002Flangsmith\u002FSKILL.md",true,[82,88],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",12006,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F17-observability\u002Flangsmith\u002Freferences\u002Fadvanced-usage.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ftroubleshooting.md",10983,"https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F17-observability\u002Flangsmith\u002Freferences\u002Ftroubleshooting.md",[93,123,156,180,204,224],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":102,"publishedAt":44},"01M6NBV8V36BCHZ1K9F7EMRJP1","ara-rigor-reviewer","Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Frigor-reviewer\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122],"reviewer","ara rigor reviewer","ARA","review","release","audit","integrity","peer review","critique","exploration","scoring","dimensions","coherence","structural","scope","native","argument","epistemic","calibration","Research Artifacts",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":138,"publishedAt":44},"01M6NBTQTZ5G21FFPMEKHQZ3R3","ara-research-manager","Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara\u002F directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fresearch-manager\u002FSKILL.md",[129,132,133],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[43,139,140,141,142,143,144,145,146,147,148,149,112,150,151,152,153,154,155,105],"manager","ara research manager","writing","directory","heuristics","how","knowledge","knowledge management","never","scanning","conversation","maintain","project","end","recording","at","claims",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":166,"publishedAt":44},"01M6NBTCRK24WXZYKBD19RAV09","ara-compiler","Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F22-agent-native-research-artifact\u002Fcompiler\u002FSKILL.md",[162,165],{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},[167,168,169,170,171,172,143,145,173,112,174,175,118,176,177,178,155,105,122,179],"compiler","ara compiler","PDF","notes","code","logs","ingestion","artifact","input","papers","directories","layer","Knowledge Extraction",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":187,"publishedAt":44},"01M6NBSXGH49PE13PF1H9P7F8N","creative-thinking-for-research","Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fcreative-thinking-for-research\u002FSKILL.md",[186],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},[43,188,189,190,191,28,192,193,27,194,195,196,197,198,199,200,201,202,29,203],"creative","thinking","Creative Thinking","creative thinking for research","reasoning","science","ideation","frameworks","research ideation","strategies","problem","Analogical Reasoning","Problem Reformulation","novel","directions","constraint",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":44},"01M6NBSNHZYM70ZAFXQNESYYBG","brainstorming-research-ideas","Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F21-research-ideation\u002Fbrainstorming-research-ideas\u002FSKILL.md",[210],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},[43,212,213,214,215,216,217,218,189,195,219,198,220,221,190,222,223,201,202,29],"brainstorming","ideas","brainstorming research ideas","discovery","projects","work","strategy","discover","impact","guides","Problem Discovery","Research Strategy",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":236,"publishedAt":44},"01M6NBS3Z3CJYH5225X73AZN3S","systems-paper-writing","Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.","https:\u002F\u002Fgithub.com\u002Fzhengliangliahu-cloud\u002Fskills\u002Fblob\u002Fmain\u002FAI-Research-SKILLs\u002F20-ml-paper-writing\u002Fsystems-paper-writing\u002FSKILL.md",[230,231,232,235],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":233,"categoryName":234,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},[141,237,238,239,240,103,241,242,243,116,244,245,246,247,176,248,29,28,249,250],"Paper Writing","systems","paper","Systems Paper Writing","guide","Blueprint","patterns","OSDI","NSDI","ASPLOS","SOSP","targeting","EuroSys","Structural Blueprint"]