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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.

47

Quality

59%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/llm-application-dev-langchain-agent/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%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 dense, largely actionable reference with good executable code, but it is a monolithic inline guide lacking progressive disclosure and explicit validation feedback loops in its build workflow. Some generic boilerplate padding could be trimmed.

Suggestions

Move large reference blocks (RAG pipeline, FastAPI deployment, evaluation suite) into separate files under references/ and link to them from SKILL.md to improve progressive disclosure.

Add explicit validation checkpoints to the build workflow (e.g., run LangSmith eval after implementation, validate graph compiles before deploy) with a fix-and-retry loop.

Trim the generic 'Instructions' boilerplate ('Clarify goals...', 'Apply best practices...') and the verbatim description repeat to reduce token padding.

DimensionReasoningScore

Conciseness

Code snippets are compact and assume Claude's competence, but the opening 'Use this skill when / Do not use this skill when / Instructions / Context' boilerplate and verbatim description repeat are generic padding that should be trimmed.

3 / 5

Actionability

Mostly copy-paste-ready Python across StateGraph, StructuredTool, FastAPI streaming, and tenacity retry, with minor gaps like the undefined stream_response, missing TypedDict/Annotated imports, and external_call placeholder.

4 / 5

Workflow Clarity

An implementation checklist and key patterns provide sequence hints, but there is no explicit validate->fix->retry feedback loop interleaved in the build workflow; for a production/batch-oriented skill the missing validation checkpoints cap this at 3.

3 / 5

Progressive Disclosure

No bundle files exist and the whole reference is inlined into SKILL.md with decent section headers but no links to separate files; content like the RAG pipeline, deployment server, and eval suite would benefit from being split into referenced files.

3 / 5

Total

13

/

20

Passed

Description

40%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 is a role statement rather than a capability/trigger description: it names the niche but provides no concrete actions and no 'Use when' clause. It is distinct but weak on specificity and completeness.

Suggestions

Replace the role framing with concrete capability verbs, e.g. 'Build, debug, and deploy production LangChain/LangGraph agents...'.

Add an explicit 'Use when...' clause naming natural user triggers like 'building a LangChain agent', 'LangGraph workflows', or 'multi-agent orchestration'.

Include natural synonym keywords (agent, LLM app, tool-using agent) and file/extension-style specifics to improve trigger-term coverage.

DimensionReasoningScore

Specificity

The description names the domain (LangChain/LangGraph, production AI) but frames it as a role ('expert...developer') with no concrete capability actions, matching the 'names domain but actions minimal/generic' anchor.

2 / 5

Completeness

It offers only a vague role-based 'what' and entirely omits any 'when'/'Use when' trigger clause; per the guidelines a missing trigger clause caps completeness, and the vague what keeps it at the lower anchor.

2 / 5

Trigger Term Quality

It includes the core library keywords ('LangChain', 'LangGraph') that users would naturally say, but is phrased as a role and lacks common variations like 'agent', 'LLM app', or 'multi-agent'.

3 / 5

Distinctiveness Conflict Risk

The specific LangChain 0.1+/LangGraph niche distinguishes it from generic coding skills with only minor overlap risk against related LLM-app skills, though triggers are library-named rather than natural phrases.

4 / 5

Total

11

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/llm-application-dev-langchain-agent/SKILL.md

Reviewed

Table of Contents

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