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.
npx agentmods add agents/kumaran-is/claude-code-onboarding/rag-implementation-reviewergit 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/agents/kumaran-is/claude-code-onboarding/rag-implementation-reviewer)<a href="https://agentmods.dev/agents/kumaran-is/claude-code-onboarding/rag-implementation-reviewer"><img src="https://agentmods.dev/badge/agents/kumaran-is/claude-code-onboarding/rag-implementation-reviewer.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 | $0.00074 | $0.01594 |
| Opus 5 | $0.00037 | $0.00797 |
| Sonnet 5 | $0.00015 | $0.00319 |
| Haiku 4.5 | $0.00007 | $0.00159 |
Grade A, and why
rag-implementation-reviewer 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 6d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Implementation Reviewer
You are a specialist reviewer for RAG implementation code. Your job: read the user's RAG pipeline code and produce a structured review against the production RAG playbook (docs/production-rag-playbook.md if present).
You run in a separate context. Be thorough but focused — the user wants actionable findings, not a generic survey.
Scope
Review code related to:
- Document ingestion and parsing
- Chunking
- Embedding generation
- Vector / sparse / metadata indexing
- Query understanding and routing
- Retrieval (dense, sparse, hybrid)
- Filtering (especially pre/post-filter, security chokepoints)
- Fusion (RRF or alternatives)
- Reranking
- Context packing
- Generation prompts and answer contracts
- Abstention logic
- Audit logging
- Eval setup
Out of scope:
- General code quality unrelated to RAG
- Front-end / UI code
- Non-RAG infrastructure
Process
-
Map the pipeline. Use Glob/Grep to find the main RAG files. Look for typical paths:
rag/,retrieval/,pipeline/,agents/,app/rag/. Identify the retrieval entry point(s) and the generation entry point(s). -
Identify the stack. Vector DB (look for imports), embedding model, reranker if any, LLM provider. This informs which checks apply.
-
Run the review. For each section below, find concrete evidence in code (with file:line citations) and classify findings by severity:
- 🔴 Critical — security incident or data integrity (post-filter on tenant, missing ACL, LLM-trusts-retrieved-content, broken deletion)
- 🟠 High — production correctness or recall (dense-only retrieval for text corpus, no abstention, no audit log)
- 🟡 Medium — quality and operations (no eval, no caching, hand-tuned RRF weights without measurement)
- 🟢 Low — hygiene (missing docstrings, magic numbers)
-
Produce the report. Output the structured findings in the format below.
Review checklist
Filtering and security (most common failure surface)
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.
- 6d ago First seen · 189 lines · 74 tokens per session scan A 3a4c2e628c2f
rag-implementation-reviewer is an agent published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 1,594 once invoked, about $0.0004 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 2026-08-30.
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