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langchain

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

60

Quality

71%

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tessl review fix ./14-agents/langchain/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a comprehensive, code-rich reference, but it is padded with marketing metrics, leans on deprecated chain APIs that contradict its own '0.3+ stable' version claim, lacks validation checkpoints in the RAG workflow, and duplicates content already housed in the reference files. Every dimension lands at 2: usable but improvable.

Suggestions

Cut marketing fluff (GitHub-star/contributor metrics, 'most popular framework' tagline, 'Performance benchmarks' latency table) and present the body as a lean overview that defers detail to the reference files, improving both conciseness and progressive_disclosure.

Replace the legacy chain APIs (LLMChain, ConversationChain, RetrievalQA, ConversationalRetrievalChain, chain.run(), load_qa_with_sources_chain) with current 0.3+ equivalents (e.g. create_agent, with_structured_output, the modern retriever/LLM compose pattern) and fix the broken WebBaseLoader usage, so code is actually executable.

Add a validation/feedback checkpoint to the RAG pipeline (e.g. inspect retrieved chunks and re-query or adjust search_kwargs if retrieval is empty) so the multi-step workflow earns a higher workflow_clarity score.

DimensionReasoningScore

Conciseness

Quotes the marketing fluff 'The most popular framework', '119,000+ GitHub stars', '3,800+ contributors', and a 'Performance benchmarks' latency table — none help Claude use the framework; the bulk is useful code, so it is mostly efficient but padded. Not score 3 because several sections are unnecessary context that could be cut; not score 1 because it does not explain basic concepts Claude already knows.

2 / 3

Actionability

Code looks copy-paste ready (e.g. the create_agent example), but much uses deprecated APIs — 'chain.run(topic=…)', 'RetrievalQA.from_chain_type', 'ConversationChain', 'ConversationalRetrievalChain', 'load_qa_with_sources_chain' — despite claiming 'Version: 0.3+', and 'WebBaseLoader'/'loader.load(url)' and 'eval(x)' are incorrect. Not score 3 because examples are not reliably executable; not score 1 because substantial concrete code is present.

2 / 3

Workflow Clarity

The RAG pipeline is clearly sequenced ('1. Load documents … 6. Query'), but there are no validation/feedback checkpoints (e.g. verify retrieval quality, confirm embeddings persisted). Not score 3 because explicit validation steps are absent; not score 1 because a concrete numbered sequence exists.

2 / 3

Progressive Disclosure

References are well-signaled and one level deep ('[RAG Guide](references/rag.md)'), and the referenced files exist, but the body duplicates that material inline (full RAG pipeline plus a RAG guide), matching the anchor 'content that should be separate is inline'. Not score 3 because the body is not a lean overview pointing to references; not score 1 because references are real, clearly signaled, and only one level deep.

2 / 3

Total

8

/

12

Passed

Description

92%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that clearly states capabilities and includes an explicit 'Use for…' trigger clause covering natural user phrasings. Its only weakness is moderate conflict risk, since the trigger terms (RAG, chatbots, agents) are shared across many LLM frameworks.

DimensionReasoningScore

Specificity

Quotes 'building LLM-powered applications with agents, chains, and RAG' and 'tool calling, memory management, and vector store retrieval' — multiple concrete capabilities enumerated, matching the score-3 anchor; not the level below because it lists several distinct concrete actions rather than naming a domain plus only one action.

3 / 3

Completeness

Answers 'what' ('Framework for building LLM-powered applications…') and 'when' via the explicit 'Use for building chatbots…' trigger clause; not capped at 2 because an explicit 'Use for…' trigger clause is present.

3 / 3

Trigger Term Quality

Quotes 'chatbots, question-answering systems, autonomous agents, or RAG applications' — these are natural terms a user would actually say; not the level below because coverage spans several common phrasings rather than a single keyword.

3 / 3

Distinctiveness Conflict Risk

The named framework (LangChain) gives a niche, but triggers like 'RAG', 'chatbots', and 'autonomous agents' overlap heavily with LlamaIndex/LangGraph/Haystack, so it could trigger for a competing skill; not score 3 because distinctiveness relies on the framework name rather than unique triggers.

2 / 3

Total

11

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
Orchestra-Research/AI-Research-SKILLs
Reviewed

Table of Contents

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