rag-review

rag-review is a command for Claude Code from kumaran-is/claude-code-onboarding. It costs 29 tokens per session (848 once invoked), scanned A, original, MIT.

An end-to-end audit of a RAG pipeline against an eight-stage production checklist. It checks how documents are processed, retrieved, answered, secured, tested, and monitored.

In plain words
What is it for?
It is for reviewing ingestion, search, answer generation, access control, evaluations, and observability before or during production work.
Why use it?
It reveals missing or incomplete parts of a RAG system in one structured report, with severity and an overall verdict. RAG means using retrieved documents to support an AI answer.

Command for Claude Code

Written for Claude Code: installed under .claude/.

Good fit It is for reviewing ingestion, search, answer generation, access control, evaluations, and observability before or during production work.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/kumaran-is/claude-code-onboarding/rag-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/kumaran-is/claude-code-onboarding

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for rag-review

README.md
[![agentmods](https://agentmods.dev/badge/commands/kumaran-is/claude-code-onboarding/rag-review.svg)](https://agentmods.dev/commands/kumaran-is/claude-code-onboarding/rag-review)
Your own site
<a href="https://agentmods.dev/commands/kumaran-is/claude-code-onboarding/rag-review"><img src="https://agentmods.dev/badge/commands/kumaran-is/claude-code-onboarding/rag-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 848 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.00848
Opus 5 $0.00015 $0.00424
Sonnet 5 $0.00006 $0.00170
Haiku 4.5 $0.00003 $0.00085

Measured 4d ago against content hash 83bcf85c44c3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

rag-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/commands/rag-review.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rag-review — E2E RAG Pipeline Audit

Skill: Load rag-review skill — it is the single source of truth for all 8-stage checks, severity rubric, verdict format, and reference cross-links. Do not restate checks inline here.

Audit a RAG pipeline against the production checklist covering all 8 stages: ingestion/chunking, versioning/updates, embedding/indexing, query routing, retrieval/fusion/reranking, generation/abstention, security, and eval/observability.

Phase 1: Discover the implementation

Walk the repo and identify:

  • Where is the RAG pipeline code? (rag/, retrieval/, pipeline/, agents/, or similar)
  • What vector DB is used? (check imports: pgvector, weaviate, qdrant, pinecone, chroma)
  • What embedding model and reranker, if any?
  • Where is the generation prompt template?
  • Is there an audit log?
  • Is there an eval directory with a golden set?

If the structure is unclear, ask the user once to point at the main pipeline entry point.

Phase 2: Run the checklist

Apply the 8-stage checklist from the rag-review skill. For each check, classify as:

  • Present — implemented correctly with evidence
  • ⚠️ Partial — exists but incomplete or misconfigured
  • Missing — not present
  • Unknown — couldn't determine from available code

For every ❌ or ⚠️ finding, cite file:line as evidence. No generic advice without a code reference.

Phase 3: Output a structured report

# RAG Pipeline Audit

**Repo:** {{repo-name}}
**Date:** {{today}}
**Stack detected:** {{vector DB | embedding model | reranker | LLM}}

## Summary

- ✅ Present: N of total
- ⚠️ Partial: N
- ❌ Missing: N
- ❓ Unknown: N

## 🔴 Critical (BLOCK)

### C1. {{Finding title}}
**Stage:** {{stage number and name}}
**Status:** ❌ Missing / ⚠️ Partial
**Evidence:** `path/to/file.py:42` — [paste the problematic code]
**Issue:** [one sentence — why this is a production risk]
**Fix:** [concrete code change or pattern]
**Reference:** `rag-review/SKILL.md §{{stage}} check {{#}}`

## 🟠 High (NEEDS_REVIEW — fix before merge)

### H1. {{Finding title}}
[same structure as Critical]

## 🟡 Medium (NEEDS_REVIEW — recommend)

[same structure]

## 🟢 Low (APPROVE with note)

[brief list — no full structure needed for Low]

## Recommended next 3 PRs (priority order)

1. **{{PR title}}** — fixes {{finding IDs}}; effort S/M/L
2. ...
3. ...

VERDICT: [APPROVE | NEEDS_REVIEW | BLOCK] — CRITICAL: N | HIGH: N | MEDIUM: N | LOW: N

Read the full file on GitHub · 95 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 95 lines · 29 tokens per session scan A 83bcf85c44c3

Subscribe to this mod's changes

rag-review is a command published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 848 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.