[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-langchain-langsmith-dataset":3,"mdc-y227pp-key":36,"related-repo-langchain-langsmith-dataset":2880,"related-org-langchain-langsmith-dataset":2913},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":34,"mdContent":35},"langsmith-dataset","create and manage LangSmith eval datasets","INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"langchain","LangChain","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Flangchain.png","langchain-ai",[13,17,20,23],{"name":14,"slug":15,"type":16},"Datasets","datasets","tag",{"name":18,"slug":19,"type":16},"LLM","llm",{"name":21,"slug":22,"type":16},"Evals","evals",{"name":24,"slug":25,"type":16},"LangSmith","langsmith",139,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-skills","2026-04-11T04:40:26.848233",null,17,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":29},[],"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-skills\u002Ftree\u002FHEAD\u002Fconfig\u002Fskills\u002Flangsmith-dataset","---\nname: langsmith-dataset\ndescription: \"INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.\"\n---\n\n\u003Coneliner>\nCreate, manage, and upload evaluation datasets to LangSmith for testing and validation.\n\u003C\u002Foneliner>\n\n\u003Csetup>\nEnvironment Variables\n\n```bash\nLANGSMITH_API_KEY=lsv2_pt_your_api_key_here          # REQUIRED\nLANGSMITH_PROJECT=your-project-name                   # Check this to know which project has traces\nLANGSMITH_WORKSPACE_ID=your-workspace-id              # Optional: for org-scoped keys\n```\n\nAuthentication is REQUIRED: either set the `LANGSMITH_API_KEY` environment variable, or pass the `--api-key` flag to CLI commands (preferred):\n```bash\nlangsmith dataset list --api-key $LANGSMITH_API_KEY\n```\n\n**IMPORTANT:** Always check the environment variables or `.env` file for `LANGSMITH_PROJECT` before querying or interacting with LangSmith. This tells you which project contains the relevant traces and data. If the LangSmith project is not available, use your best judgement to identify the right one.\n\nPython Dependencies\n```bash\npip install langsmith\n```\n\nJavaScript Dependencies\n```bash\nnpm install langsmith\n```\n\nCLI Tool\n\n```bash\ncurl -sSL https:\u002F\u002Fraw.githubusercontent.com\u002Flangchain-ai\u002Flangsmith-cli\u002Fmain\u002Fscripts\u002Finstall.sh | sh\n```\n\u003C\u002Fsetup>\n\n\u003Cusage>\nUse the `langsmith` CLI to manage datasets and examples.\n\n### Dataset Commands\n\n- `langsmith dataset list` - List datasets in LangSmith\n- `langsmith dataset get \u003Cname-or-id>` - View dataset details\n- `langsmith dataset create --name \u003Cname>` - Create a new empty dataset\n- `langsmith dataset delete \u003Cname-or-id>` - Delete a dataset\n- `langsmith dataset export \u003Cname-or-id> \u003Coutput-file>` - Export dataset to local JSON file\n- `langsmith dataset upload \u003Cfile> --name \u003Cname>` - Upload a local JSON file as a dataset\n\n### Example Commands\n\n- `langsmith example list --dataset \u003Cname>` - List examples in a dataset\n- `langsmith example create --dataset \u003Cname> --inputs \u003Cjson>` - Add an example to a dataset\n- `langsmith example delete \u003Cexample-id>` - Delete an example\n\n### Experiment Commands\n\n- `langsmith experiment list --dataset \u003Cname>` - List experiments for a dataset\n- `langsmith experiment get \u003Cname>` - View experiment results\n\n### Common Flags\n\n- `--limit N` - Limit number of results\n- `--yes` - Skip confirmation prompts (use with caution)\n\n**IMPORTANT - Safety Prompts:**\n- The CLI prompts for confirmation before destructive operations (delete, overwrite)\n- **If you are running with user input:** ALWAYS wait for user input; NEVER use `--yes` unless the user explicitly requests it\n- **If you are running non-interactively:** Use `--yes` to skip confirmation prompts\n\u003C\u002Fusage>\n\n\u003Cdataset_types_overview>\nCommon evaluation dataset types:\n\n- **final_response** - Full conversation with expected output. Tests complete agent behavior.\n- **single_step** - Single node inputs\u002Foutputs. Tests specific node behavior (e.g., one LLM call or tool).\n- **trajectory** - Tool call sequence. Tests execution path (ordered list of tool names).\n- **rag** - Question\u002Fchunks\u002Fanswer\u002Fcitations. Tests retrieval quality.\n\u003C\u002Fdataset_types_overview>\n\n\u003Ccreating_datasets>\n## Creating Datasets\n\nDatasets are JSON files with an array of examples. Each example has `inputs` and `outputs`.\n\n### From Exported Traces (Programmatic)\n\nExport traces first, then process them into dataset format using code:\n\n```bash\n# 1. Export traces to JSONL files\nlangsmith trace export .\u002Ftraces --project my-project --limit 20 --full --api-key $LANGSMITH_API_KEY\n```\n\n\u003Cpython>\n```python\nimport json\nfrom pathlib import Path\nfrom langsmith import Client\n\nclient = Client()\n\n# 2. Process traces into dataset examples\nexamples = []\nfor jsonl_file in Path(\".\u002Ftraces\").glob(\"*.jsonl\"):\n    runs = [json.loads(line) for line in jsonl_file.read_text().strip().split(\"\\n\")]\n    root = next((r for r in runs if r.get(\"parent_run_id\") is None), None)\n    if root and root.get(\"inputs\") and root.get(\"outputs\"):\n        examples.append({\n            \"trace_id\": root.get(\"trace_id\"),\n            \"inputs\": root[\"inputs\"],\n            \"outputs\": root[\"outputs\"]\n        })\n\n# 3. Save locally\nwith open(\"\u002Ftmp\u002Fdataset.json\", \"w\") as f:\n    json.dump(examples, f, indent=2)\n```\n\u003C\u002Fpython>\n\n\u003Ctypescript>\n```typescript\nimport { Client } from \"langsmith\";\nimport { readFileSync, writeFileSync, readdirSync } from \"fs\";\nimport { join } from \"path\";\n\nconst client = new Client();\n\n\u002F\u002F 2. Process traces into dataset examples\nconst examples: Array\u003C{trace_id?: string, inputs: Record\u003Cstring, any>, outputs: Record\u003Cstring, any>}> = [];\nconst files = readdirSync(\".\u002Ftraces\").filter(f => f.endsWith(\".jsonl\"));\n\nfor (const file of files) {\n  const lines = readFileSync(join(\".\u002Ftraces\", file), \"utf-8\").trim().split(\"\\n\");\n  const runs = lines.map(line => JSON.parse(line));\n  const root = runs.find(r => r.parent_run_id == null);\n  if (root?.inputs && root?.outputs) {\n    examples.push({ trace_id: root.trace_id, inputs: root.inputs, outputs: root.outputs });\n  }\n}\n\n\u002F\u002F 3. Save locally\nwriteFileSync(\"\u002Ftmp\u002Fdataset.json\", JSON.stringify(examples, null, 2));\n```\n\u003C\u002Ftypescript>\n\n### Upload to LangSmith\n\n```bash\n# Upload local JSON file as a dataset\nlangsmith dataset upload \u002Ftmp\u002Fdataset.json --name \"My Evaluation Dataset\" --api-key $LANGSMITH_API_KEY\n```\n\n### Using the SDK Directly\n\n\u003Cpython>\n```python\nfrom langsmith import Client\n\nclient = Client()\n\n# Create dataset and add examples in one step\ndataset = client.create_dataset(\"My Dataset\", description=\"Evaluation dataset\")\n\nclient.create_examples(\n    inputs=[{\"query\": \"What is AI?\"}, {\"query\": \"Explain RAG\"}],\n    outputs=[{\"answer\": \"AI is...\"}, {\"answer\": \"RAG is...\"}],\n    dataset_name=\"My Dataset\",\n)\n```\n\u003C\u002Fpython>\n\n\u003Ctypescript>\n```typescript\nimport { Client } from \"langsmith\";\n\nconst client = new Client();\n\n\u002F\u002F Create dataset and add examples\nconst dataset = await client.createDataset(\"My Dataset\", {\n  description: \"Evaluation dataset\",\n});\n\nawait client.createExamples({\n  inputs: [{ query: \"What is AI?\" }, { query: \"Explain RAG\" }],\n  outputs: [{ answer: \"AI is...\" }, { answer: \"RAG is...\" }],\n  datasetName: \"My Dataset\",\n});\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fcreating_datasets>\n\n\u003Cdataset_structures>\n## Dataset Structures by Type\n\n### Final Response\n```json\n{\"trace_id\": \"...\", \"inputs\": {\"query\": \"What are the top genres?\"}, \"outputs\": {\"response\": \"The top genres are...\"}}\n```\n\n### Single Step\n```json\n{\"trace_id\": \"...\", \"inputs\": {\"messages\": [...]}, \"outputs\": {\"content\": \"...\"}, \"metadata\": {\"node_name\": \"model\"}}\n```\n\n### Trajectory\n```json\n{\"trace_id\": \"...\", \"inputs\": {\"query\": \"...\"}, \"outputs\": {\"expected_trajectory\": [\"tool_a\", \"tool_b\", \"tool_c\"]}}\n```\n\n### RAG\n```json\n{\"trace_id\": \"...\", \"inputs\": {\"question\": \"How do I...\"}, \"outputs\": {\"answer\": \"...\", \"retrieved_chunks\": [\"...\"], \"cited_chunks\": [\"...\"]}}\n```\n\u003C\u002Fdataset_structures>\n\n\u003Cscript_usage>\n## CLI Usage\n\n```bash\n# List all datasets\nlangsmith dataset list --api-key $LANGSMITH_API_KEY\n\n# Get dataset details\nlangsmith dataset get \"My Dataset\" --api-key $LANGSMITH_API_KEY\n\n# Create an empty dataset\nlangsmith dataset create --name \"New Dataset\" --description \"For evaluation\" --api-key $LANGSMITH_API_KEY\n\n# Upload a local JSON file\nlangsmith dataset upload \u002Ftmp\u002Fdataset.json --name \"My Dataset\" --api-key $LANGSMITH_API_KEY\n\n# Export a dataset to local file\nlangsmith dataset export \"My Dataset\" \u002Ftmp\u002Fexported.json --limit 100 --api-key $LANGSMITH_API_KEY\n\n# Delete a dataset\nlangsmith dataset delete \"My Dataset\" --api-key $LANGSMITH_API_KEY\n\n# List examples in a dataset\nlangsmith example list --dataset \"My Dataset\" --limit 10 --api-key $LANGSMITH_API_KEY\n\n# Add an example\nlangsmith example create --dataset \"My Dataset\" \\\n  --inputs '{\"query\": \"test\"}' \\\n  --outputs '{\"answer\": \"result\"}' --api-key $LANGSMITH_API_KEY\n\n# List experiments\nlangsmith experiment list --dataset \"My Dataset\" --api-key $LANGSMITH_API_KEY\nlangsmith experiment get \"eval-v1\" --api-key $LANGSMITH_API_KEY\n```\n\u003C\u002Fscript_usage>\n\n\u003Cexample_workflow>\nComplete workflow from traces to uploaded LangSmith dataset:\n\n```bash\n# 1. Export traces from LangSmith\nlangsmith trace export .\u002Ftraces --project my-project --limit 20 --full --api-key $LANGSMITH_API_KEY\n\n# 2. Process traces into dataset format (using Python\u002FJS code)\n# See \"Creating Datasets\" section above\n\n# 3. Upload to LangSmith\nlangsmith dataset upload \u002Ftmp\u002Ffinal_response.json --name \"Skills: Final Response\" --api-key $LANGSMITH_API_KEY\nlangsmith dataset upload \u002Ftmp\u002Ftrajectory.json --name \"Skills: Trajectory\" --api-key $LANGSMITH_API_KEY\n\n# 4. Verify upload\nlangsmith dataset list --api-key $LANGSMITH_API_KEY\nlangsmith dataset get \"Skills: Final Response\" --api-key $LANGSMITH_API_KEY\nlangsmith example list --dataset \"Skills: Final Response\" --limit 3 --api-key $LANGSMITH_API_KEY\n\n# 5. Run experiments\nlangsmith experiment list --dataset \"Skills: Final Response\" --api-key $LANGSMITH_API_KEY\n```\n\u003C\u002Fexample_workflow>\n\n\u003Ctroubleshooting>\n**Dataset upload fails:**\n- Verify LANGSMITH_API_KEY is set\n- Check JSON file is valid: each element needs `inputs` (and optionally `outputs`)\n- Dataset name must be unique, or delete existing first with `langsmith dataset delete`\n\n**Empty dataset after upload:**\n- Verify JSON file contains an array of objects with `inputs` key\n- Check file isn't empty: `langsmith example list --dataset \"Name\"`\n\n**Export has no data:**\n- Ensure traces were exported with `--full` flag to include inputs\u002Foutputs\n- Verify traces have both `inputs` and `outputs` populated\n\n**Example count mismatch:**\n- Use `langsmith dataset get \"Name\"` to check remote count\n- Compare with local file to verify upload completeness\n\u003C\u002Ftroubleshooting>\n\u003C\u002Foutput>\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,49,323,2874],{"type":42,"tag":43,"props":44,"children":45},"element","oneliner",{},[46],{"type":47,"value":48},"text","\nCreate, manage, and upload evaluation datasets to LangSmith for testing and validation.\n",{"type":42,"tag":50,"props":51,"children":52},"setup",{},[53,55,143,164,199,225,230,255,260,283,288],{"type":47,"value":54},"\nEnvironment Variables\n",{"type":42,"tag":56,"props":57,"children":62},"pre",{"className":58,"code":59,"language":60,"meta":61,"style":61},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","LANGSMITH_API_KEY=lsv2_pt_your_api_key_here          # 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Use this skill when the user asks to: (1) create a new skill, (2) make a skill, (3) build a skill, (4) set up a skill, (5) initialize a skill, (6) scaffold a skill, (7) update or modify an existing skill, (8) validate a skill, (9) learn about skill structure, (10) understand how skills work, or (11) get guidance on skill design patterns. Trigger on phrases like \"create a skill\", \"new skill\", \"make a skill\", \"skill for X\", \"how do I create a skill\", or \"help me build a skill\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3032,3033,3036],{"name":2984,"slug":2985,"type":16},{"name":3034,"slug":3035,"type":16},"Engineering","engineering",{"name":3037,"slug":3038,"type":16},"Plugin Development","plugin-development","2026-05-13T06:10:59.88449",{"slug":3041,"name":3041,"fn":3042,"description":3043,"org":3044,"tags":3045,"stars":2933,"repoUrl":2934,"updatedAt":3049},"social-media","create optimized social media posts","Create social media posts optimized for engagement across platforms.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3046,3047,3048],{"name":2954,"slug":2955,"type":16},{"name":2922,"slug":2923,"type":16},{"name":2961,"slug":2962,"type":16},"2026-04-15T05:00:55.37452",{"slug":3051,"name":3051,"fn":3052,"description":3053,"org":3054,"tags":3055,"stars":2933,"repoUrl":2934,"updatedAt":3060},"web-research","conduct and synthesize web research","Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3056,3057,3058,3059],{"name":2984,"slug":2985,"type":16},{"name":3008,"slug":3009,"type":16},{"name":2925,"slug":2926,"type":16},{"name":2944,"slug":2945,"type":16},"2026-05-13T06:11:04.930044",{"slug":3062,"name":3062,"fn":3063,"description":3064,"org":3065,"tags":3066,"stars":3077,"repoUrl":3078,"updatedAt":3079},"mermaid-diagrams","embed Mermaid diagrams in documentation","Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3067,3070,3071,3074],{"name":3068,"slug":3069,"type":16},"Diagrams","diagrams",{"name":3002,"slug":3003,"type":16},{"name":3072,"slug":3073,"type":16},"Markdown","markdown",{"name":3075,"slug":3076,"type":16},"Technical Writing","technical-writing",12181,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopenwiki","2026-07-24T06:09:01.089597",{"slug":3081,"name":3081,"fn":3082,"description":3083,"org":3084,"tags":3085,"stars":3077,"repoUrl":3078,"updatedAt":3092},"write-connector","implement OpenWiki source connectors","Add a new built-in OpenWiki source connector. Use when a user asks to create or implement an OpenWiki connector.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3086,3089],{"name":3087,"slug":3088,"type":16},"API Development","api-development",{"name":3090,"slug":3091,"type":16},"Integrations","integrations","2026-07-18T05:48:23.961804",41]