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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

48%

Does it follow best practices?

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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

50%Weight 40%Scale 1-3

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, well-organized single-file reference with strong code coverage, but it is monolithic, has incomplete snippets, and lacks validation feedback loops in its workflow. Splitting detailed material into reference files would substantially improve it.

Suggestions

Extract the RAG pipeline, production deployment, and testing sections into separate reference files (e.g. references/rag.md, references/deployment.md) and link to them from SKILL.md.

Make code snippets executable by including the necessary imports and replacing placeholder functions (external_call, node1_func, stream_response) with concrete implementations.

Add explicit validation checkpoints to the build workflow (e.g. 'run eval suite and only proceed when scores pass') to support feedback loops for production changes.

DimensionReasoningScore

Conciseness

The body is mostly lean code and terse bullets without over-explaining basics, but ~245 inline lines with some redundancy (error handling appears in multiple sections) could be tightened.

2 / 3

Actionability

It provides many concrete code blocks, but several are incomplete — missing imports (TypedDict, Annotated, HumanMessage) and placeholder calls (external_call, stream_response, node1_func) — so it falls short of fully copy-paste ready.

2 / 3

Workflow Clarity

A checklist and sequenced patterns are present, but the build/deploy workflow has no explicit validation checkpoints or validate→fix→retry feedback loops for risky production operations, capping it at 2.

2 / 3

Progressive Disclosure

Content is well-sectioned but entirely monolithic — all RAG, deployment, and reference material is inline with no bundle files (references/scripts/assets are absent) and no one-level-deep pointers to split it out.

2 / 3

Total

8

/

12

Passed

Description

47%Weight 40%Scale 1-3

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 establishes a clear niche but is written in second person, names no concrete actions, and lacks an explicit 'Use when' trigger clause. It is adequate but generic for routing purposes.

Suggestions

Rewrite in third person and name concrete actions, e.g. 'Builds, orchestrates, and deploys LangChain/LangGraph agents with async patterns, memory, and RAG pipelines.'

Add an explicit trigger clause: 'Use when building or debugging LangChain 0.1+ or LangGraph agents, multi-agent workflows, or RAG pipelines.'

Replace the 'You are an expert...' framing with descriptive capability language to satisfy the voice requirement and improve specificity.

DimensionReasoningScore

Specificity

The phrase 'production-grade AI systems using LangChain 0.1+ and LangGraph' names the domain but lists no concrete actions; additionally the second-person 'You are an expert...' voice triggers the rubric's -1 specificity penalty, pulling this from a 2 down to 1.

1 / 3

Completeness

It states what the skill does (expert LangChain/LangGraph agent development) but provides no 'Use when...' clause or explicit trigger guidance, which the rubric caps at 2.

2 / 3

Trigger Term Quality

'LangChain' and 'LangGraph' are relevant keywords a user might say, but the description misses common variations like 'LangChain agent', 'LangGraph workflow', or 'agent orchestration'.

2 / 3

Distinctiveness Conflict Risk

The LangChain/LangGraph niche is specific and unlikely to trigger for unrelated skills, matching the 'clear niche with distinct triggers' anchor.

3 / 3

Total

8

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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