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llm-application-dev-langchain-agent

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

Quality

55%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-langchain-agent/SKILL.md

The canonical home for this skill is llm-application-dev-langchain-agent in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

38%Weight 40%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 body is a broad, code-rich catalog of LangChain/LangGraph patterns, but it is monolithic: heavy inline detail that belongs in reference files, a broken pointer to a nonexistent playbook, and redundant requirement/checklist/best-practice lists. Code snippets are concrete but frequently non-executable due to missing imports and undefined variables.

Suggestions

Split the pattern catalogs (Memory Systems, RAG Pipeline, Production Deployment, Testing & Evaluation, Key Patterns) into one-level-deep reference files and keep SKILL.md as a concise overview with clearly signaled links.

Fix or remove the dangling `resources/implementation-playbook.md` reference — the file does not exist in the bundle.

Deduplicate the overlapping "Core Requirements", "Implementation Checklist", and "Best Practices" sections into a single sequenced build workflow with validation checkpoints, and cut generic patterns Claude already knows (tenacity retries, try/except, FastAPI streaming).

Make code snippets executable: add missing imports (TypedDict, Annotated, RunEvalConfig) and define placeholder variables (index, checkpointer, llm) or mark sections as fragments explicitly.

DimensionReasoningScore

Conciseness

Several padded or redundant sections: circular filler like "Working on langchain/langchain agent development expert tasks", the same guidance repeated across "Core Requirements", "Implementation Checklist", "Best Practices", and "Optimization Strategies", and generic patterns Claude already knows (tenacity retry, try/except error handling, FastAPI streaming). It is not 3 because these unnecessary sections are numerous rather than incidental, and not 1 because the code examples are concrete and much of the prose is terse bullets rather than explanatory padding.

2 / 5

Actionability

Concrete code exists ("builder = StateGraph(MessagesState)", the RAG pipeline, StructuredTool, and LangSmith eval snippets), but many fragments are not executable: "class AgentState(TypedDict)" uses TypedDict/Annotated without imports, and `index`, `checkpointer`, `llm`, `prompt`, and `stream_response` are referenced but never defined. The instruction "open `resources/implementation-playbook.md`" points to a file that does not exist in the bundle, so it is not 4 where gaps would be minor.

3 / 5

Workflow Clarity

The "Implementation Checklist" provides a rough build sequence and verification appears only as late items ("Write evaluation tests", "Setup health checks"), so steps are listed but inter-step validation checkpoints are missing. It is not 2 because a real sequence and verification activities do exist, and not 4 because no explicit checkpoints or feedback loops connect the steps.

3 / 5

Progressive Disclosure

Roughly 150 lines of pattern catalog (memory systems, RAG, deployment, monitoring, testing, key patterns) are inlined in SKILL.md where they belong in reference files, and the single external pointer "open `resources/implementation-playbook.md`" is buried in Instructions and references a file that does not exist in the bundle (no references/, scripts/, or assets/ directories are present). It is above anchor 1 because the body has clear section headers and is navigable, but below anchor 3 because the one reference present is neither clearly signaled nor valid.

2 / 5

Total

10

/

20

Passed

Description

51%Weight 40%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.

The description names a specific technical niche (LangChain/LangGraph agent development) but is written as a second-person persona statement rather than a capability description. It completely lacks a 'when to use' clause and any concrete action verbs.

Suggestions

Rewrite in third person with concrete actions, e.g. "Builds, debugs, and deploys production LangChain/LangGraph agents with async patterns, memory systems, RAG pipelines, and LangSmith observability."

Add an explicit trigger clause, e.g. "Use when the user asks to build, refactor, or deploy agents or multi-agent systems with LangChain, LangGraph, RAG pipelines, or tool calling."

Include natural trigger variations users would say (RAG, chatbot, LLM application, multi-agent orchestration) to improve trigger term coverage.

DimensionReasoningScore

Specificity

The description names the domain ("LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph") but lists no concrete actions, matching the anchor-2 example of minimal/generic actions; the rubric's third-person rule penalizes second person ("You are an expert") by 1, reducing it to 1. It is not anchor 1 on its own merits because it does name specific frameworks rather than being pure abstraction.

1 / 5

Completeness

A clear 'what' is present (production-grade LangChain/LangGraph agent systems), but the 'when' is entirely absent with no "Use when..." clause or equivalent trigger guidance, capping this at 3 per the rubric guideline. It is not 4 because there is no explicit or even weakly implied use condition.

3 / 5

Trigger Term Quality

"LangChain 0.1+", "LangGraph", and "agent" are terms users would naturally say when they need this skill, giving good keyword coverage. It falls short of anchor 5 because natural variations like "RAG", "chatbot", "LLM app", "tool calling", or "multi-agent" are missing.

4 / 5

Distinctiveness Conflict Risk

"LangChain" and "LangGraph" are distinctive niche trigger terms with only minor overlap risk against closely related LLM-app development skills. It is not 5 because "production-grade AI systems" is broad enough to invite overlap with generic AI/agent skills, and there is no explicit trigger framing to disambiguate.

4 / 5

Total

12

/

20

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.

Validation — 15 / 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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-langchain-agent/SKILL.md

Reviewed

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