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.
git clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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.
[](https://agentmods.dev/commands/kumaran-is/claude-code-onboarding/rag-review)<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>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.
| Model | Per session | Once 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 |
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.
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
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.
- 4d ago First seen · 95 lines · 29 tokens per session scan A 83bcf85c44c3
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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
inference.embed
Embed one or more texts into vectors.
vector.batch_exists
Check existence for multiple vector keys.
vector.batch_upsert
Upsert multiple vectors in one itemwise batch.