jbvc/langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
85
85%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly identifies the domain (LangGraph), lists specific capabilities, and includes an explicit 'Use when' clause with relevant trigger terms. The only minor weaknesses are the marketing-style claims ('production-grade', 'Used in production at LinkedIn, Uber, and 400+ companies') which add noise without aiding skill selection, but overall the description is well-structured and distinctive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. | 3 / 3 |
Completeness | Clearly answers both 'what' (building stateful multi-actor AI applications with specific capabilities listed) and 'when' (explicit 'Use when:' clause with trigger terms). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'langgraph', 'langchain agent', 'stateful agent', 'agent graph', 'react agent'. Also includes contextual terms like 'LangChain', 'human-in-the-loop', and 'checkpointers' in the body. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche around LangGraph specifically. The trigger terms are domain-specific ('langgraph', 'langchain agent', 'agent graph') and unlikely to conflict with general coding or other AI framework skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides solid, executable code examples for core LangGraph patterns and is well-structured with clear pattern names and use-case guidance. However, it's somewhat verbose with unnecessary meta-sections (Capabilities, Requirements, Limitations), lacks validation/debugging workflows despite acknowledging debugging challenges, and promises coverage of persistence, human-in-the-loop, and streaming patterns that are never delivered.
Suggestions
Remove or drastically trim the Capabilities, Requirements, and Limitations sections—these don't provide actionable guidance and waste tokens on things Claude already knows.
Add a validation/debugging workflow showing how to visualize the graph (app.get_graph().draw_mermaid()) and test individual nodes, especially since the skill acknowledges debugging is challenging.
Add concrete examples for checkpointers/persistence and human-in-the-loop patterns (mentioned in the description and capabilities but completely absent from the content), either inline or as referenced external files.
Split advanced patterns (persistence, human-in-the-loop, streaming) into separate referenced files to improve progressive disclosure and keep the main skill focused on core graph construction.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary sections like 'Capabilities' (a bullet list of features Claude doesn't need), 'Requirements' (explaining Python 3.9+ and 'understanding of graph concepts'), and 'Limitations' that add little actionable value. The code examples themselves are reasonably lean but the surrounding prose could be tightened significantly. | 2 / 3 |
Actionability | The code examples are fully executable and copy-paste ready, covering the core patterns (basic agent, state with reducers, conditional branching). Each pattern includes complete imports, state definitions, node functions, graph construction, and invocation. The anti-patterns section provides concrete code fixes. | 3 / 3 |
Workflow Clarity | The basic agent graph has numbered steps (1-7) which provide good sequencing, but there are no validation checkpoints, error handling steps, or feedback loops for debugging. For a framework where 'debugging can be challenging' (as the skill itself notes), the absence of validation/verification steps is a notable gap. | 2 / 3 |
Progressive Disclosure | The content is structured with clear sections and patterns, but it's a long monolithic file (~200+ lines of code examples) with no references to external files for advanced topics like persistence/checkpointers, human-in-the-loop, streaming, or async execution—all listed in capabilities but never covered. These would benefit from separate reference files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
Reviewed
Table of Contents