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llm-application-dev

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

80%

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

A lean, highly actionable catalog of executable LLM-development patterns with no concept padding. It is weaker on workflow structure (no validation checkpoints) and progressive disclosure (a long single-file catalog that could be split into reference files).

Suggestions

Add explicit validation checkpoints to the multi-step workflows — e.g., after retrieval in the RAG pipeline, verify the returned documents are non-empty/relevant before generating, and surface a fallback when context is empty.

Split the larger pattern groups (RAG, API integration) into one-level-deep reference files (e.g., references/rag.md, references/api-patterns.md) with clearly signaled links from SKILL.md, keeping the main file as a concise overview.

For destructive or batch operations (e.g., bulk embedding storage), add a verify-then-proceed feedback loop so failures are caught before continuation.

DimensionReasoningScore

Conciseness

The body is almost entirely executable code with brief section headers and inline comments, assuming Claude's competence and avoiding concept explanations that Claude already knows. Not a 2 because there is no padded prose to tighten.

3 / 3

Actionability

Provides fully executable, copy-paste-ready TypeScript for OpenAI/Anthropic calls, streaming, a RAG pipeline, chunking, embedding storage, and retry-with-backoff error handling — concrete and specific throughout.

3 / 3

Workflow Clarity

The RAG pipeline is numbered (embed -> search -> build context -> generate) and the error handler has a retry loop, but there are no explicit validation checkpoints or error-recovery feedback loops for the operations as a whole. Not a 3 because checkpoints are implicit; not a 1 because sequences are present.

2 / 3

Progressive Disclosure

A single ~218-line file of code reference with clear sections but no external references; substantial pattern catalogs (RAG, API integration) sit inline where they could be split into one-level-deep reference files, matching the '200 lines of API reference that could be in a separate file' anchor. Not a 3 because content is not split across signaled references; not a 1 because sections are well organized with no nested references.

2 / 3

Total

10

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12

Passed

Description

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.

A strong description: third-person, concrete capabilities, and an explicit 'Use for' trigger clause covering natural user terms. It cleanly answers both what the skill does and when to invoke it.

DimensionReasoningScore

Specificity

Names multiple concrete capabilities — 'prompt engineering, RAG patterns, and LLM integration' — rather than vague language, matching the anchor that lists several specific actions.

3 / 3

Completeness

Explicitly answers both what ('Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration') and when (an explicit 'Use for...' trigger clause).

3 / 3

Trigger Term Quality

'Use for AI-powered features, chatbots, or LLM-based automation' uses natural terms a user would actually say, with good coverage of common variations.

3 / 3

Distinctiveness Conflict Risk

The LLM-application niche with triggers like 'chatbots' and 'LLM-based automation' is distinct and unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation15 / 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
MoizIbnYousaf/Ai-Agent-Skills
Reviewed

Table of Contents

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