[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-langchain-ai-langchain-rag-ar":3,"guides-for-langchain-ai-langchain-rag":265,"similar-k177161763y7wpe7pwpz9hj47h867c2v":266},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":22,"identity":191,"isFallback":196,"parentExtension":197,"providers":243,"relations":246,"repo":247,"workflow":264},1778053782268.2002,"k177161763y7wpe7pwpz9hj47h867c2v",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":12,"sourceUrl":13,"tags":14},"INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).",{},"LangChain RAG","https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/langchain-rag",[15,16,17,18,19,20,21],"rag","langchain","llm","embeddings","vectorstore","python","typescript",{"_creationTime":23,"_id":24,"extensionId":5,"locale":25,"result":26,"trustSignals":180,"workflow":189},1778053822111.3633,"kn74shpvnzv6hzbvahzhc9phmn86759n","en",{"checks":27,"evaluatedAt":170,"extensionSummary":171,"promptVersionExtension":172,"promptVersionScoring":173,"rationale":174,"score":175,"summary":176,"tags":177,"targetMarket":178,"tier":179},[28,33,36,39,43,46,50,55,58,61,65,70,73,77,80,83,86,89,92,95,99,103,107,112,116,119,122,125,129,132,135,138,141,144,148,151,154,157,160,163,167],{"category":29,"check":30,"severity":31,"summary":32},"Practical Utility","Problem relevance","pass","The description clearly states the problem of building retrieval-augmented generation (RAG) systems and names specific components involved.",{"category":29,"check":34,"severity":31,"summary":35},"Unique selling proposition","The skill offers specific implementations for RAG components like document loaders, splitters, embeddings, and vector stores, providing value beyond a basic prompt.",{"category":29,"check":37,"severity":31,"summary":38},"Production readiness","The skill provides complete RAG pipelines with examples for Python and TypeScript, covering document loading, splitting, embedding, storing, retrieval, and generation, suitable for production use.",{"category":40,"check":41,"severity":31,"summary":42},"Scope","Single responsibility principle","The skill focuses on the RAG pipeline and its core components, without venturing into unrelated domains like deployment or testing.",{"category":40,"check":44,"severity":31,"summary":45},"Description quality","The displayed description is concise, accurate, and reflects the skill's capabilities in building RAG systems.",{"category":47,"check":48,"severity":31,"summary":49},"Invocation","Scoped tools","The examples provided demonstrate specific, narrow tools and configurations for RAG components rather than a single generalist execution tool.",{"category":51,"check":52,"severity":53,"summary":54},"Documentation","Configuration & parameter reference","info","While examples show configurations for chunk size, overlap, and vector stores, explicit defaults and precedence order for all parameters are not fully documented.",{"category":40,"check":56,"severity":31,"summary":57},"Tool naming","Tools and configurations shown in examples are descriptive and relevant to the RAG pipeline (e.g., 'RecursiveCharacterTextSplitter', 'InMemoryVectorStore').",{"category":40,"check":59,"severity":31,"summary":60},"Minimal I/O surface","Examples demonstrate focused inputs and outputs for RAG components, requesting only necessary data and returning relevant information.",{"category":62,"check":63,"severity":53,"summary":64},"License","License usability","License is not explicitly declared in the manifest files or a dedicated LICENSE file, and is only mentioned in the README footer as 'MIT'.",{"category":66,"check":67,"severity":68,"summary":69},"Maintenance","Commit recency","not_applicable","No commit history is available for this specific skill file, so recency cannot be evaluated.",{"category":66,"check":71,"severity":68,"summary":72},"Dependency Management","No third-party dependencies are explicitly managed within this skill's scope; dependencies are assumed to be handled by the broader LangChain ecosystem.",{"category":74,"check":75,"severity":68,"summary":76},"Security","Secret Management","The skill does not appear to handle secrets directly; it relies on environment variables or external configuration for API keys, which are assumed to be managed securely by the user.",{"category":74,"check":78,"severity":31,"summary":79},"Injection","The skill provides code examples that treat external data as content to be processed, not as executable instructions. No runtime downloading of external content is demonstrated.",{"category":74,"check":81,"severity":31,"summary":82},"Transitive Supply-Chain Grenades","The skill's code examples are self-contained and do not involve runtime downloads or execution of external scripts, mitigating supply-chain risks.",{"category":74,"check":84,"severity":31,"summary":85},"Sandbox Isolation","The skill's examples operate within the context of LangChain's execution environment and do not attempt to modify files outside of the project's scope.",{"category":74,"check":87,"severity":31,"summary":88},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were found in the provided code examples.",{"category":74,"check":90,"severity":31,"summary":91},"Data Exfiltration","The code examples do not show any outbound calls for data exfiltration; API calls are assumed to be for RAG functionality.",{"category":74,"check":93,"severity":31,"summary":94},"Hidden Text Tricks","The bundled content is free of hidden-steering tricks, and descriptions are clean printable ASCII.",{"category":96,"check":97,"severity":31,"summary":98},"Hooks","Opaque code execution","The provided code examples are plain, readable source code and do not involve obfuscation or runtime code fetching.",{"category":100,"check":101,"severity":31,"summary":102},"Portability","Structural Assumption","Examples use relative paths or placeholders (e.g., './document.pdf', './chroma_db'), making them portable across different project structures.",{"category":104,"check":105,"severity":68,"summary":106},"Trust","Issues Attention","No issues data is available for this specific skill file.",{"category":108,"check":109,"severity":110,"summary":111},"Versioning","Release Management","warning","No explicit version information (e.g., `version:` in SKILL.md frontmatter, `package.json`, or CHANGELOG) is present for this skill; installation via 'npx skills' or plugin marketplace does not allow for version pinning.",{"category":113,"check":114,"severity":53,"summary":115},"Code Execution","Validation","While parameters like chunk size and overlap are shown, explicit schema validation for all inputs and outputs is not demonstrated in the provided examples.",{"category":74,"check":117,"severity":68,"summary":118},"Unguarded Destructive Operations","The skill focuses on data processing and retrieval, with no inherently destructive operations demonstrated.",{"category":113,"check":120,"severity":53,"summary":121},"Error Handling","Error handling is not explicitly demonstrated in the provided examples, though LangChain's general error handling mechanisms are assumed.",{"category":113,"check":123,"severity":68,"summary":124},"Logging","The skill does not involve destructive actions or outbound calls that would necessitate local audit logging.",{"category":126,"check":127,"severity":68,"summary":128},"Compliance","GDPR","The skill does not directly handle personal data; it processes documents and embeddings, assuming data sanitization is managed upstream or by the user.",{"category":126,"check":130,"severity":31,"summary":131},"Target market","No regional signals were detected in the prompts or code examples; the RAG pipeline is globally applicable.",{"category":100,"check":133,"severity":31,"summary":134},"Runtime stability","The examples provided are written using standard LangChain libraries and common language constructs (Python/TypeScript) and do not appear to make OS-specific assumptions.",{"category":47,"check":136,"severity":31,"summary":137},"Precise Purpose","The description clearly states the skill is for building RAG systems and names specific components, providing clear positive triggers and boundaries.",{"category":47,"check":139,"severity":31,"summary":140},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core capability and scope.",{"category":51,"check":142,"severity":31,"summary":143},"Concise Body","The SKILL.md body is well-structured, uses progressive disclosure for detailed examples, and stays within reasonable length.",{"category":145,"check":146,"severity":31,"summary":147},"Context","Progressive Disclosure","Detailed examples and configurations are embedded directly within the SKILL.md, which is acceptable given the scope and length.",{"category":145,"check":149,"severity":68,"summary":150},"Forked exploration","This skill is not an exploration-style skill; it provides implementations for RAG components rather than deep code analysis.",{"category":29,"check":152,"severity":31,"summary":153},"Usage examples","Sufficient end-to-end examples are provided for Python and TypeScript, demonstrating input, invocation, and plausible outcomes for various RAG components.",{"category":29,"check":155,"severity":53,"summary":156},"Edge cases","Failure modes like dimension mismatch are hinted at in the 'fix' sections, but not explicitly documented with symptoms and recovery paths within the main skill instructions.",{"category":113,"check":158,"severity":68,"summary":159},"Tool Fallback","The skill uses core LangChain components and does not appear to rely on optional external tools like an MCP server with a fallback.",{"category":100,"check":161,"severity":31,"summary":162},"Stack assumptions","The skill explicitly shows examples for Python and TypeScript, and the underlying libraries are generally cross-platform.",{"category":164,"check":165,"severity":68,"summary":166},"Safety","Halt on unexpected state","This skill does not involve destructive operations or complex state management that would require explicit halting on unexpected pre-state.",{"category":100,"check":168,"severity":31,"summary":169},"Cross-skill coupling","The skill focuses on RAG components and does not implicitly rely on other skills or quietly handle adjacent tasks.",1778053811046,"This skill facilitates the construction of RAG pipelines by offering implementations for document loading, text splitting, embedding generation, and various vector stores including Chroma, FAISS, and Pinecone. It showcases end-to-end examples in both Python and TypeScript, detailing the setup, retrieval, and generation stages.","2.0.0","3.4.0","The skill provides a well-structured and practical implementation of RAG components with clear examples for both Python and TypeScript. While there's a minor lack of explicit error handling documentation and versioning, the overall quality, scope, and security posture are high, leading to a 'verified' tier.",85,"Provides a comprehensive set of tools and examples for building Retrieval Augmented Generation (RAG) systems using LangChain.",[15,16,17,18,19,20,21],"global","verified",{"codeQuality":181,"collectedAt":182,"documentation":183,"maintenance":185,"security":186,"testCoverage":188},{},1778053800092,{"descriptionLength":184,"readmeSize":8},203,{},{"hasNpmPackage":187,"smitheryVerified":187},false,{"hasCi":187,"hasTests":187},{"updatedAt":190},1778053822111,{"githubOwner":192,"githubRepo":193,"locale":25,"slug":194,"type":195},"langchain-ai","langchain-skills","langchain-rag","skill",true,{"_creationTime":198,"_id":199,"community":200,"display":201,"identity":212,"parentExtension":214,"providers":237,"relations":241,"workflow":242},1778053782268.1963,"k1710b29xhgdykchws1tmnjrnx866nxv",{"reviewCount":8},{"description":202,"installMethods":203,"name":204,"sourceUrl":205,"tags":206},"Agent skills for building agents with LangChain, LangGraph, and Deep Agents",{},"LangChain Skills","https://github.com/langchain-ai/langchain-skills",[16,207,208,209,15,210,211],"langgraph","deep-agents","ai-agents","skills","plugins",{"githubOwner":192,"githubRepo":193,"locale":25,"slug":193,"type":213},"plugin",{"_creationTime":215,"_id":216,"community":217,"display":218,"identity":221,"providers":223,"relations":232,"workflow":234},1778053782268.1958,"k1787v8f6tx7dxy8c4xfz7hk2h867f5j",{"reviewCount":8},{"description":202,"installMethods":219,"name":204,"sourceUrl":205,"tags":220},{},[16,207,208,209,15],{"githubOwner":192,"githubRepo":193,"locale":25,"slug":193,"type":222},"marketplace",{"extract":224,"llm":229},{"commitSha":225,"license":226,"marketplace":227},"648df5daa32ef7a742a07740d9c5d13e3b8229c4","MIT",{"name":193,"pluginCount":228},1,{"promptVersionExtension":172,"promptVersionScoring":173,"score":230,"targetMarket":178,"tier":231},70,"evaluated",{"repoId":233},"kd74nzm561ws7px2qx08z27fad864yft",{"anyEnrichmentAt":235,"extractAt":236,"githubAt":235,"llmAt":190,"updatedAt":190},1778053782949,1778053782268,{"extract":238,"llm":239},{"commitSha":225,"license":226},{"promptVersionExtension":172,"promptVersionScoring":173,"score":240,"targetMarket":178,"tier":231},75,{"parentExtensionId":216,"repoId":233},{"anyEnrichmentAt":235,"extractAt":236,"githubAt":235,"llmAt":190,"updatedAt":190},{"extract":244,"llm":245},{"commitSha":225,"license":226},{"promptVersionExtension":172,"promptVersionScoring":173,"score":175,"targetMarket":178,"tier":179},{"parentExtensionId":199,"repoId":233},{"_creationTime":248,"_id":233,"identity":249,"providers":250,"workflow":261},1777995558409.8423,{"githubOwner":192,"githubRepo":193,"sourceUrl":205},{"discover":251,"github":254},{"sources":252},[253],"skills-sh",{"closedIssues90d":255,"forks":256,"openIssues90d":8,"pushedAt":257,"readmeSize":258,"stars":259,"topics":260},2,58,1777486877000,3660,651,[],{"discoverAt":262,"extractAt":263,"githubAt":263,"updatedAt":263},1777995558409,1778053783581,{"anyEnrichmentAt":235,"extractAt":236,"githubAt":235,"llmAt":190,"updatedAt":190},[],[267,295,328],{"_creationTime":268,"_id":269,"community":270,"display":271,"identity":281,"providers":285,"relations":289,"workflow":291},1778053730743.9438,"k177p296xm96wbqmez2dfcytk18662fm",{"reviewCount":8},{"description":272,"installMethods":273,"name":274,"sourceUrl":275,"tags":276},"Help users build effective AI applications. 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