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rag-engineer

I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out. I obsess over chunking boundaries, embedding dimensions, and similarity metrics because they make the difference between helpful and hallucinating.

Quality

54%

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SecuritybySnyk

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tessl review fix ./plugins/antigravity-bundle-agent-architect/skills/rag-engineer/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

43%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 reads as a well-intentioned RAG knowledge card: real technique names and a useful sharp-edges table, but no executable code, stub solutions, empty anti-pattern entries, and no end-to-end workflow with validation steps. It catalogs what matters in RAG without instructing how to do any of it.

Suggestions

Replace the fenced bullet lists with actual executable examples (e.g., a short pdfplumber/langchain chunking snippet, a BM25 + vector + RRF fusion snippet) and complete the sharp-edges Solution column entries, which currently end in bare colons.

Add a sequenced workflow (ingest → chunk → embed → index → retrieve → rerank → evaluate) with an explicit validation checkpoint for measuring retrieval quality separately from generation.

Remove the duplicated Role paragraph, the Requirements section, and the circular 'When to Use' sentence; either give each anti-pattern a one-line explanation or fold them into the sharp-edges table.

DimensionReasoningScore

Conciseness

The body is short and mostly lean, but there is clear tightening opportunity: the 'Role' paragraph repeats the frontmatter description verbatim, the 'Requirements' section lists concepts Claude already knows ('LLM fundamentals', 'Basic NLP concepts') as non-actionable filler, and 'When to Use' is a single circular filler sentence. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than 4, which would require only minor trimming — here three separate sections add little or no value.

3 / 5

Actionability

Real technique names appear (BM25/TF-IDF, Reciprocal Rank Fusion, parent-child chunk relationships, two-pass retrieval), giving some concrete guidance, but the fenced 'javascript' blocks contain only bullet-point pseudocode — no executable code, no library calls, no thresholds or parameters. The sharp-edges table's Solution column entries are stubs ending in bare colons ('Use semantic chunking that respects document structure:') with no actual solution content. This fits the anchor of some concrete guidance but incomplete, pseudocode instead of executable code, missing key details.

3 / 5

Workflow Clarity

The body is a catalog of capabilities, patterns, and pitfalls with no sequenced process — there is no guidance on how to actually proceed (e.g., ingest → chunk → embed → index → retrieve → rerank → evaluate), and no validation or measurement steps despite the anti-pattern 'Ignoring Evaluation' and the high-severity 'Not measuring retrieval quality' sharp edge. A rough two-pass hierarchy is mentioned inside one pattern, but the skill's own workflow has many gaps and no checkpoints, matching the anchor for rough sequence with poorly defined steps rather than the anchor for steps-listed-with-validation-gaps.

2 / 5

Progressive Disclosure

Section headers are present and the content is a single self-contained file with no broken or nested references (no bundle files exist), but organization is mixed: the 'Anti-Patterns' entries are empty headers with no explanation, 'When to Use' is circular filler, and code fences are misused as bullet-list containers. This matches 'some structure but could be better organized' — it does not reach 4 because several sections are structural stubs rather than appropriately placed content.

3 / 5

Total

11

/

20

Passed

Description

45%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 conveys the RAG domain through personality-driven, first-person prose but never states concrete capabilities, omits the skill's own most natural trigger term ('RAG'), and provides no 'when to use' guidance. It functions as a persona bio rather than a skill description, leaving trigger routing and expectation-setting underserved.

Suggestions

Rewrite in third person as concrete capability statements, e.g. 'Designs RAG retrieval pipelines: chunks documents, selects embedding models, and tunes hybrid (BM25 + vector) search.'

Add an explicit trigger clause: 'Use when building or debugging retrieval-augmented generation (RAG), vector search, semantic search, or knowledge-base pipelines.'

Include the natural terms users actually say — 'RAG', 'retrieval-augmented generation', 'vector database', 'knowledge base' — alongside the existing technical vocabulary.

DimensionReasoningScore

Specificity

The description names specific technical areas — 'chunking boundaries, embedding dimensions, and similarity metrics' — which is more concrete than a bare domain label, but it states no actual capabilities or actions (no verbs like 'design retrieval pipelines' or 'chunk documents'), only identity statements like 'I bridge the gap between raw documents and LLM understanding.' Base score 3 is reduced by 1 because the entire description uses first-person voice ('I bridge', 'I know', 'I obsess') in violation of the third-person guideline.

2 / 5

Completeness

A 'what' is present but framed as identity/philosophy ('bridge the gap between raw documents and LLM understanding... make the difference between helpful and hallucinating') rather than concrete function, and the 'when' is entirely missing — there is no 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the judging guideline. It sits at 3 rather than 4 because the 'when' is absent rather than merely imprecise, but the domain 'what' is at least conveyed.

3 / 5

Trigger Term Quality

Terms like 'retrieval quality', 'chunking', 'embedding', 'similarity metrics', and 'hallucinating' are relevant to the domain, but the most natural trigger users would say — 'RAG' or 'retrieval-augmented generation' — is entirely absent, as are 'vector database', 'semantic search', and 'knowledge base'. This matches the anchor of some relevant keywords while missing common variations and synonyms.

3 / 5

Distinctiveness Conflict Risk

'Chunking boundaries, embedding dimensions, and similarity metrics' point fairly clearly at the retrieval-pipeline niche, but without the term 'RAG' the description overlaps with semantic-search, vector-database, embeddings, and NLP skills — somewhat specific but could still overlap with similar skills. It does not reach 4 because nothing in the text pins it to a clear, distinct trigger set that would prevent mis-triggering against those neighbors.

3 / 5

Total

11

/

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-agent-architect/skills/rag-engineer/SKILL.md

Reviewed

Table of Contents

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