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rag-implementation

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

Quality

59%

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SecuritybySnyk

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tessl review fix ./plugins/antigravity-bundle-llm-application-developer/skills/rag-implementation/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

35%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 presents a well-sequenced eight-phase RAG workflow but is almost entirely abstract scaffolding: no code, commands, parameters, or measurable criteria anywhere, padded with repetitive per-phase boilerplate and trivial delegation prompts. It reads as a table of contents for work rather than instructions for doing the work.

Suggestions

Replace vague actions with executable specifics: example code or commands for each phase (e.g., a chunking snippet with concrete chunk-size/overlap values, a sample vector DB schema or index config, an evaluation command with expected metrics).

Give the Quality Gates measurable pass criteria and add validate-fix-retry checkpoints between phases (e.g., "retrieval hit@k >= X on the test set before proceeding to LLM integration").

Cut the "Copy-Paste Prompts" boilerplate and collapse the repetitive per-phase structure; if detail is needed, move per-phase guidance into one-level-deep reference files in a references/ directory.

DimensionReasoningScore

Conciseness

While it never explains concepts Claude already knows, the body is padded scaffolding: eight near-identical phase blocks, trivial one-line "Copy-Paste Prompts" like "Use @rag-engineer to implement chunking strategy", and action lists of bare verbs that add almost no information, matching 'noticeably verbose; several unnecessary or padded sections'. Not score 1 because there is no basic-concept re-teaching (the anchor-1 failure mode); not score 3 because the padding is pervasive across every phase rather than incidental.

2 / 5

Actionability

Every action is a high-level directive with no executable content — "Choose vector database", "Implement chunking", "Set up reranking" — with no code, commands, parameters, or concrete criteria anywhere; this fits 'minimal concrete guidance; high-level hints but missing the specific steps to execute'. Not score 1 because the phases do provide a rough ordered roadmap of steps rather than a single vague directive; not score 3 because there is no executable code or command at all, not even partially complete examples.

2 / 5

Workflow Clarity

The eight phases are clearly sequenced, but validation is absent throughout: the "Quality Gates" are unchecked checkboxes with no thresholds or criteria ("Evaluation passing" — passing what?), and no feedback loops exist between phases, matching 'steps listed but validation gaps; checkpoints missing or implicit'. Not score 2 because the sequence is complete and well-defined rather than gapped; not score 4 because checkpoints are entirely missing rather than minor.

3 / 5

Progressive Disclosure

The skill is a single ~190-line file with no bundle files at all; section headers per phase give some structure, but the repetitive inline phase boilerplate and "Skills to Invoke" cross-skill mentions provide no navigational value, matching 'some structure but could be better organized'. Not score 2 because nothing is buried or inlined that clearly belongs in a separate file at this thin level of detail; not score 4 because the monolithic repetitive structure is a clear organization gap and there are no references to signal.

3 / 5

Total

10

/

20

Passed

Description

66%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 domain and enumerates four concrete components with natural trigger terms, but it entirely lacks a 'when to use' clause, which both caps completeness and leaves the trigger guidance implicit. Coverage is good but not comprehensive relative to what the skill body actually addresses.

Suggestions

Append an explicit trigger clause, e.g. "Use when building RAG pipelines, semantic search, or document Q&A systems, or when the user mentions retrieval-augmented generation, embeddings, or vector databases."

Add natural user phrasings such as "semantic search", "chat with my documents", or "knowledge-grounded AI" to broaden trigger term coverage.

Mention the remaining lifecycle stages the body covers (LLM integration, caching, evaluation) so the 'what' is comprehensive, and lead the components with verbs ("selects embeddings, sets up vector DBs, implements chunking...") to sharpen specificity.

DimensionReasoningScore

Specificity

The description lists four concrete components ("embedding selection, vector database setup, chunking strategies, and retrieval optimization") in a clearly named domain, but they are topic nouns rather than actions and omit areas the body covers (LLM integration, caching, evaluation), matching the 'several specific actions; minor gaps' anchor rather than the comprehensive 5.

4 / 5

Completeness

It clearly answers 'what' (an implementation workflow covering four named components) but contains no 'Use when...' clause or equivalent explicit trigger guidance; per the rubric guideline, a missing 'when' caps completeness at 3.

3 / 5

Trigger Term Quality

"RAG (Retrieval-Augmented Generation)", "embedding", "vector database", "chunking", and "retrieval" are terms users would naturally say, but common variations like "semantic search", "knowledge base", or "chat with documents" are missing, which fits the 'good keyword coverage; a few natural terms missing' anchor.

4 / 5

Distinctiveness Conflict Risk

RAG implementation is a clear niche with distinct triggers, but the component phrases "embedding selection" and "vector database setup" overlap with closely related dedicated skills, matching 'mostly distinct; minor overlap risk' rather than the minimal-conflict 5.

4 / 5

Total

15

/

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/antigravity-bundle-llm-application-developer/skills/rag-implementation/SKILL.md

Reviewed

Table of Contents

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