[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-langchain-langchain-python-quickstart":3,"mdc--u7prsf-key":31,"related-repo-langchain-langchain-python-quickstart":220,"related-org-langchain-langchain-python-quickstart":327},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":21,"repoUrl":22,"updatedAt":23,"license":24,"forks":25,"topics":26,"repo":27,"sourceUrl":29,"mdContent":30},"langchain-python-quickstart","scaffold local LangChain agents in Python","Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.",{"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",[13,15,18],{"name":9,"slug":8,"type":14},"tag",{"name":16,"slug":17,"type":14},"Python","python",{"name":19,"slug":20,"type":14},"Agents","agents",877,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills","2026-07-24T06:09:05.588823",null,77,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":24},[],"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills\u002Ftree\u002FHEAD\u002Fconfig\u002Fskills\u002Flangchain-python-quickstart","---\nname: langchain-python-quickstart\ndescription: \"Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.\"\n---\n\n# LangChain Python quickstart\n\nFollow the live docs — do not invent an alternate API from memory:\n\n**https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flangchain\u002Fquickstart**\n\nFetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + `create_agent`).\n\n## Local setup constraints\n\nApply these on top of the quickstart (they keep setup minimal and model-agnostic):\n\n1. **Ask** which provider\u002Fmodel to use. Showcase that LangChain is model-agnostic. Suggested prompt:\n\n   > Which model should this agent use? Pass a `provider:model` string — e.g. `openai:gpt-5.5`, `anthropic:claude-sonnet-5`, `google_genai:gemini-2.5-flash-lite`. Default if you're unsure: **`anthropic:claude-sonnet-5`**.\n\n   Swap the quickstart's model string for their choice (or the default).\n\n2. Create a **new** directory (e.g. `langchain-agent\u002F`) and do all work there — do not pollute the open project.\n\n3. Only secret: the provider API key in `.env` (gitignored). No LangSmith \u002F Tavily unless they ask. Prefer they edit `.env` themselves — don't paste keys into chat.\n\n4. Install the provider package needed for their model if the quickstart's base install isn't enough.\n\n5. Run the example, show output, then stop. Point to `langchain-fundamentals` for next steps.\n",{"data":32,"body":33},{"name":4,"description":6},{"type":34,"children":35},"root",[36,44,50,65,79,86,91],{"type":37,"tag":38,"props":39,"children":40},"element","h1",{"id":4},[41],{"type":42,"value":43},"text","LangChain Python quickstart",{"type":37,"tag":45,"props":46,"children":47},"p",{},[48],{"type":42,"value":49},"Follow the live docs — do not invent an alternate API from memory:",{"type":37,"tag":45,"props":51,"children":52},{},[53],{"type":37,"tag":54,"props":55,"children":56},"strong",{},[57],{"type":37,"tag":58,"props":59,"children":63},"a",{"href":60,"rel":61},"https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flangchain\u002Fquickstart",[62],"nofollow",[64],{"type":42,"value":60},{"type":37,"tag":45,"props":66,"children":67},{},[68,70,77],{"type":42,"value":69},"Fetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + ",{"type":37,"tag":71,"props":72,"children":74},"code",{"className":73},[],[75],{"type":42,"value":76},"create_agent",{"type":42,"value":78},").",{"type":37,"tag":80,"props":81,"children":83},"h2",{"id":82},"local-setup-constraints",[84],{"type":42,"value":85},"Local setup constraints",{"type":37,"tag":45,"props":87,"children":88},{},[89],{"type":42,"value":90},"Apply these on top of the quickstart (they keep setup minimal and model-agnostic):",{"type":37,"tag":92,"props":93,"children":94},"ol",{},[95,162,182,202,207],{"type":37,"tag":96,"props":97,"children":98},"li",{},[99,104,106,156,160],{"type":37,"tag":54,"props":100,"children":101},{},[102],{"type":42,"value":103},"Ask",{"type":42,"value":105}," which provider\u002Fmodel to use. Showcase that LangChain is model-agnostic. Suggested prompt:",{"type":37,"tag":107,"props":108,"children":109},"blockquote",{},[110],{"type":37,"tag":45,"props":111,"children":112},{},[113,115,121,123,129,131,137,138,144,146,154],{"type":42,"value":114},"Which model should this agent use? Pass a ",{"type":37,"tag":71,"props":116,"children":118},{"className":117},[],[119],{"type":42,"value":120},"provider:model",{"type":42,"value":122}," string — e.g. ",{"type":37,"tag":71,"props":124,"children":126},{"className":125},[],[127],{"type":42,"value":128},"openai:gpt-5.5",{"type":42,"value":130},", ",{"type":37,"tag":71,"props":132,"children":134},{"className":133},[],[135],{"type":42,"value":136},"anthropic:claude-sonnet-5",{"type":42,"value":130},{"type":37,"tag":71,"props":139,"children":141},{"className":140},[],[142],{"type":42,"value":143},"google_genai:gemini-2.5-flash-lite",{"type":42,"value":145},". Default if you're unsure: ",{"type":37,"tag":54,"props":147,"children":148},{},[149],{"type":37,"tag":71,"props":150,"children":152},{"className":151},[],[153],{"type":42,"value":136},{"type":42,"value":155},".",{"type":37,"tag":157,"props":158,"children":159},"br",{},[],{"type":42,"value":161},"Swap the quickstart's model string for their choice (or the default).",{"type":37,"tag":96,"props":163,"children":164},{},[165,167,172,174,180],{"type":42,"value":166},"Create a ",{"type":37,"tag":54,"props":168,"children":169},{},[170],{"type":42,"value":171},"new",{"type":42,"value":173}," directory (e.g. ",{"type":37,"tag":71,"props":175,"children":177},{"className":176},[],[178],{"type":42,"value":179},"langchain-agent\u002F",{"type":42,"value":181},") and do all work there — do not pollute the open project.",{"type":37,"tag":96,"props":183,"children":184},{},[185,187,193,195,200],{"type":42,"value":186},"Only secret: the provider API key in ",{"type":37,"tag":71,"props":188,"children":190},{"className":189},[],[191],{"type":42,"value":192},".env",{"type":42,"value":194}," (gitignored). No LangSmith \u002F Tavily unless they ask. Prefer they edit ",{"type":37,"tag":71,"props":196,"children":198},{"className":197},[],[199],{"type":42,"value":192},{"type":42,"value":201}," themselves — don't paste keys into chat.",{"type":37,"tag":96,"props":203,"children":204},{},[205],{"type":42,"value":206},"Install the provider package needed for their model if the quickstart's base install isn't enough.",{"type":37,"tag":96,"props":208,"children":209},{},[210,212,218],{"type":42,"value":211},"Run the example, show output, then stop. Point to ",{"type":37,"tag":71,"props":213,"children":215},{"className":214},[],[216],{"type":42,"value":217},"langchain-fundamentals",{"type":42,"value":219}," for next steps.",{"items":221,"total":326},[222,237,252,270,280,292,308],{"slug":223,"name":223,"fn":224,"description":225,"org":226,"tags":227,"stars":21,"repoUrl":22,"updatedAt":236},"deep-agents-core","build applications with LangChain Deep Agents","INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[228,229,232,233],{"name":19,"slug":20,"type":14},{"name":230,"slug":231,"type":14},"Architecture","architecture",{"name":9,"slug":8,"type":14},{"name":234,"slug":235,"type":14},"LLM","llm","2026-04-06T18:26:24.822592",{"slug":238,"name":238,"fn":239,"description":240,"org":241,"tags":242,"stars":21,"repoUrl":22,"updatedAt":251},"deep-agents-memory","implement memory and persistence for Deep Agents","INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[243,244,245,248],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":246,"slug":247,"type":14},"Memory","memory",{"name":249,"slug":250,"type":14},"Storage","storage","2026-04-06T18:26:26.065654",{"slug":253,"name":253,"fn":254,"description":255,"org":256,"tags":257,"stars":21,"repoUrl":22,"updatedAt":269},"deep-agents-orchestration","orchestrate subagents and tasks in Deep Agents","INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[258,259,262,263,266],{"name":19,"slug":20,"type":14},{"name":260,"slug":261,"type":14},"Approvals","approvals",{"name":9,"slug":8,"type":14},{"name":264,"slug":265,"type":14},"Multi-Agent","multi-agent",{"name":267,"slug":268,"type":14},"Workflow Automation","workflow-automation","2026-04-06T18:26:32.280463",{"slug":271,"name":271,"fn":272,"description":273,"org":274,"tags":275,"stars":21,"repoUrl":22,"updatedAt":279},"deepagents-python-quickstart","scaffold local Deep Agents","Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[276,277,278],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":16,"slug":17,"type":14},"2026-07-24T06:09:00.291108",{"slug":281,"name":281,"fn":282,"description":283,"org":284,"tags":285,"stars":21,"repoUrl":22,"updatedAt":291},"deepagents-typescript-quickstart","build Deep Agents in TypeScript","Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[286,287,288],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":289,"slug":290,"type":14},"TypeScript","typescript","2026-07-24T06:09:00.673714",{"slug":293,"name":293,"fn":294,"description":295,"org":296,"tags":297,"stars":21,"repoUrl":22,"updatedAt":307},"ecosystem-primer","select frameworks for LangChain and LangGraph agents","INVOKE FIRST for any LangChain \u002F LangGraph \u002F Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[298,299,302,303,304],{"name":19,"slug":20,"type":14},{"name":300,"slug":301,"type":14},"AI Infrastructure","ai-infrastructure",{"name":230,"slug":231,"type":14},{"name":9,"slug":8,"type":14},{"name":305,"slug":306,"type":14},"LangGraph","langgraph","2026-07-24T05:42:48.348966",{"slug":309,"name":309,"fn":310,"description":311,"org":312,"tags":313,"stars":21,"repoUrl":22,"updatedAt":325},"eval-engineering","create and audit Harbor agent evals","Iteratively inspect an agent repository and optional user-provided traces, interview the user, and create, run, and audit Harbor evals one at a time. Use for agent evals, Harbor tasks, benchmark cases, verifier design, or controlled agent environments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[314,315,318,321,322],{"name":19,"slug":20,"type":14},{"name":316,"slug":317,"type":14},"Code Analysis","code-analysis",{"name":319,"slug":320,"type":14},"Evals","evals",{"name":9,"slug":8,"type":14},{"name":323,"slug":324,"type":14},"Testing","testing","2026-07-31T05:53:29.552458",22,{"items":328,"total":499},[329,350,361,378,391,406,419,432,446,456,467,486],{"slug":330,"name":330,"fn":331,"description":332,"org":333,"tags":334,"stars":347,"repoUrl":348,"updatedAt":349},"analyze-market","perform market analysis and size estimation","Perform a market analysis for a product category or segment. Trigger on: market analysis, market size, TAM SAM SOM, market opportunity, industry analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[335,338,341,344],{"name":336,"slug":337,"type":14},"Marketing","marketing",{"name":339,"slug":340,"type":14},"Research","research",{"name":342,"slug":343,"type":14},"Sales","sales",{"name":345,"slug":346,"type":14},"Strategy","strategy",26592,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents","2026-04-18T04:46:54.557115",{"slug":351,"name":351,"fn":352,"description":353,"org":354,"tags":355,"stars":347,"repoUrl":348,"updatedAt":360},"arxiv-search","search arXiv for academic research papers","Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[356,357],{"name":339,"slug":340,"type":14},{"name":358,"slug":359,"type":14},"Search","search","2026-05-13T06:11:01.203061",{"slug":362,"name":362,"fn":363,"description":364,"org":365,"tags":366,"stars":347,"repoUrl":348,"updatedAt":377},"blog-post","write SEO-optimized blog posts","Write long-form blog posts with SEO optimization and clear structure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[367,370,371,374],{"name":368,"slug":369,"type":14},"Content Creation","content-creation",{"name":336,"slug":337,"type":14},{"name":372,"slug":373,"type":14},"SEO","seo",{"name":375,"slug":376,"type":14},"Writing","writing","2026-04-15T05:00:54.149813",{"slug":379,"name":379,"fn":380,"description":381,"org":382,"tags":383,"stars":347,"repoUrl":348,"updatedAt":390},"competitor-analysis","analyze competitors and market positioning","Analyze competitors in a given market segment. Trigger on: competitive landscape, competitor analysis, market comparison, competitive positioning.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[384,387,388,389],{"name":385,"slug":386,"type":14},"Competitive Intelligence","competitive-intelligence",{"name":336,"slug":337,"type":14},{"name":339,"slug":340,"type":14},{"name":345,"slug":346,"type":14},"2026-04-18T04:46:55.79306",{"slug":392,"name":392,"fn":393,"description":394,"org":395,"tags":396,"stars":347,"repoUrl":348,"updatedAt":405},"deepagents-thread-inspector","inspect local Deep Agents conversation threads","Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~\u002F.deepagents\u002F.state\u002Fsessions.db and a thread UUID or prefix.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[397,398,401,402],{"name":19,"slug":20,"type":14},{"name":399,"slug":400,"type":14},"Debugging","debugging",{"name":9,"slug":8,"type":14},{"name":403,"slug":404,"type":14},"SQLite","sqlite","2026-07-24T06:08:57.102689",{"slug":407,"name":407,"fn":408,"description":409,"org":410,"tags":411,"stars":347,"repoUrl":348,"updatedAt":418},"langgraph-docs","build stateful agents with LangGraph","Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[412,413,416,417],{"name":19,"slug":20,"type":14},{"name":414,"slug":415,"type":14},"Documentation","documentation",{"name":305,"slug":306,"type":14},{"name":264,"slug":265,"type":14},"2026-05-13T06:11:03.650877",{"slug":420,"name":420,"fn":421,"description":422,"org":423,"tags":424,"stars":347,"repoUrl":348,"updatedAt":431},"remember","capture knowledge into persistent memory","Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture learnings.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[425,426,427,430],{"name":19,"slug":20,"type":14},{"name":414,"slug":415,"type":14},{"name":428,"slug":429,"type":14},"Knowledge Management","knowledge-management",{"name":246,"slug":247,"type":14},"2026-05-13T06:10:58.510037",{"slug":433,"name":433,"fn":434,"description":435,"org":436,"tags":437,"stars":347,"repoUrl":348,"updatedAt":445},"skill-creator","create agent skills and tool integrations","Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. 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},[438,439,442],{"name":19,"slug":20,"type":14},{"name":440,"slug":441,"type":14},"Engineering","engineering",{"name":443,"slug":444,"type":14},"Plugin Development","plugin-development","2026-05-13T06:10:59.88449",{"slug":447,"name":447,"fn":448,"description":449,"org":450,"tags":451,"stars":347,"repoUrl":348,"updatedAt":455},"social-media","create optimized social media posts","Create social media posts optimized for engagement across platforms.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[452,453,454],{"name":368,"slug":369,"type":14},{"name":336,"slug":337,"type":14},{"name":375,"slug":376,"type":14},"2026-04-15T05:00:55.37452",{"slug":457,"name":457,"fn":458,"description":459,"org":460,"tags":461,"stars":347,"repoUrl":348,"updatedAt":466},"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},[462,463,464,465],{"name":19,"slug":20,"type":14},{"name":264,"slug":265,"type":14},{"name":339,"slug":340,"type":14},{"name":358,"slug":359,"type":14},"2026-05-13T06:11:04.930044",{"slug":468,"name":468,"fn":469,"description":470,"org":471,"tags":472,"stars":483,"repoUrl":484,"updatedAt":485},"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},[473,476,477,480],{"name":474,"slug":475,"type":14},"Diagrams","diagrams",{"name":414,"slug":415,"type":14},{"name":478,"slug":479,"type":14},"Markdown","markdown",{"name":481,"slug":482,"type":14},"Technical Writing","technical-writing",12181,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopenwiki","2026-07-24T06:09:01.089597",{"slug":487,"name":487,"fn":488,"description":489,"org":490,"tags":491,"stars":483,"repoUrl":484,"updatedAt":498},"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},[492,495],{"name":493,"slug":494,"type":14},"API Development","api-development",{"name":496,"slug":497,"type":14},"Integrations","integrations","2026-07-18T05:48:23.961804",41]