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langgraph

You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.

41

Quality

41%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/langgraph/SKILL.md

The canonical home for this skill is langgraph in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

46%Scale 1-5

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

The skill provides reasonably actionable code examples for LangGraph patterns but suffers from verbosity, explaining concepts Claude already knows and including sections of marginal value (Capabilities, Requirements, Limitations). The workflow guidance lacks validation checkpoints and error recovery steps, and all content is crammed into a single file without progressive disclosure or supporting references.

Suggestions

Remove the Capabilities, Requirements, and Limitations sections — Claude already knows these concepts and they waste tokens. Trim inline comments that explain obvious behavior.

Complete the incomplete code examples (e.g., the State with Reducers pattern's '# ... add edges') and wrap anti-pattern code in proper code blocks.

Add explicit validation steps to workflows, such as verifying graph compilation, testing with a simple input before production use, and checking for common errors like missing edges.

Extract the detailed code patterns into a separate PATTERNS.md file and keep SKILL.md as a concise overview with links to the detailed examples.

DimensionReasoningScore

Conciseness

The skill is noticeably verbose. It repeats the role description from the frontmatter, includes a 'Capabilities' list and 'Requirements' section that explain things Claude already knows (Python 3.9+, understanding of graph concepts), and the code examples include many comments explaining obvious things like '# add_messages reducer appends, doesn't overwrite'. The 'Limitations' and 'Related Skills' sections add little actionable value.

2 / 5

Actionability

The code examples are mostly executable and copy-paste ready, covering basic agent graphs, state with reducers, and conditional branching. However, some examples are incomplete (the State with Reducers pattern ends with '# ... add edges'), and the anti-patterns section uses plain text instead of proper code blocks. The tool implementations are stubs rather than real implementations.

4 / 5

Workflow Clarity

The patterns show clear sequences (define state → define tools → create LLM → define nodes → build graph → compile → run), and the anti-patterns section addresses infinite loops with exit conditions. However, there are no explicit validation checkpoints, no error handling workflows, and no feedback loops for debugging or verifying that the compiled graph works correctly before deployment.

3 / 5

Progressive Disclosure

All content is inlined in a single monolithic file with no references to supporting files. The three full code examples, anti-patterns, and all guidance are packed into one document. The capabilities list, requirements, and limitations could be omitted or separated. There are no bundle files, and the 'Related Skills' section references other skills but provides no navigable links.

2 / 5

Total

11

/

20

Passed

Description

36%Scale 1-5

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 description reads as a persona/identity statement ('You are an expert...') rather than a functional skill description. It uses second person voice, lacks concrete actions the skill performs, and has no 'Use when...' clause to guide skill selection. The LangGraph specificity helps with distinctiveness but the overall description is too abstract to be effective.

Suggestions

Rewrite in third person with concrete actions, e.g., 'Builds production-grade AI agents using LangGraph, including designing state graphs, implementing reducers, adding tool nodes, configuring persistence, and setting up human-in-the-loop workflows.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about LangGraph, building AI agents, agentic workflows, graph-based orchestration, or multi-step agent pipelines.'

Replace the persona framing ('You are an expert...') with capability-focused language that describes what the skill does rather than who it is.

DimensionReasoningScore

Specificity

Names the domain (AI agents with LangGraph) but actions are vague and abstract — 'design state carefully', 'use reducers appropriately', 'consider persistence' are more philosophical statements than concrete actions the skill performs.

2 / 5

Completeness

Provides a vague 'what' (building AI agents with LangGraph) but has no explicit 'when' clause or trigger guidance. The description reads more like a persona statement than a skill description.

2 / 5

Trigger Term Quality

Includes relevant keywords like 'LangGraph', 'AI agents', 'graphs', 'state', 'reducers', and 'persistence', but misses natural user phrases like 'build an agent', 'workflow', 'graph-based', 'multi-step', 'tool calling', or 'agentic workflow'.

3 / 5

Distinctiveness Conflict Risk

LangGraph is a specific framework which helps distinguish it, but the broad mention of 'AI agents' and 'production-grade' could overlap with other agent-building or general AI development skills.

3 / 5

Total

10

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20

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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