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 skills add lyonzin/knowledge-rag --skill rag-code-reviewgit clone --depth 1 https://github.com/lyonzin/knowledge-ragWrote 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/skills/lyonzin/knowledge-rag/rag-code-review)<a href="https://agentmods.dev/skills/lyonzin/knowledge-rag/rag-code-review"><img src="https://agentmods.dev/badge/skills/lyonzin/knowledge-rag/rag-code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lyonzin/knowledge-rag/rag-code-review"><img src="https://agentmods.dev/badge/skills/lyonzin/knowledge-rag/rag-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.01766 |
| Opus 5.5 | $0.00033 | $0.00706 |
| Sonnet 5.5 | $0.00017 | $0.00353 |
| Haiku 4.5 | $0.00008 | $0.00177 |
Grade A, and why
rag-code-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 1mo 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag-code-review — review against team standards, not the internet
When to use this skill
Trigger when the user asks:
- "Review this PR / code / diff"
- "Any issues with this?"
- "Does this look right?"
- "Is this idiomatic?"
- "Should I merge this?"
- Any critique-style prompt on a code artifact
What this skill commits to
Before offering ANY review comment, the agent consults the corpus for:
- ADRs governing the area (auth, retries, error handling, naming, dependencies…)
- Existing patterns — similar files that show the "how we do this" convention
- Prior incidents touching the code being changed
Review comments then read as "per ADR-XXXX we do Y here" rather than "generally you should Z".
Steps
-
Identify the "area" of the change. From the diff / snippet, extract:
- The file path or module name (e.g.
services/payment/,mcp_server/security.py) - The concern touched (auth, retries, logging, config, ingestion, search, storage…)
- Any new dependencies, endpoints, or public API changes
- The file path or module name (e.g.
-
Search for governing ADRs / standards:
search_knowledge(query="<concern> ADR standard", max_results=5)Example:
search_knowledge(query="retry policy ADR") -
Find similar existing files (patterns to follow):
search_similar(filepath="<the file being changed>", max_results=5)If the file is new, use
search_knowledgewith the file's purpose instead. -
Look for prior incidents / postmortems on adjacent code:
search_knowledge(query="<component> incident postmortem", max_results=3) -
Structure the review comments:
### Findings 1. **[required]** Per ADR-0031 [docs/adr/0031-retries.md] the retry policy is "3 attempts, exponential backoff". This PR uses 5 attempts — either match the ADR or open a follow-up ADR to document the exception. 2. **[suggestion]** The error-handling shape here differs from what `services/payment/refund_service.py` does [search_similar hit]. Consider aligning for consistency. 3. **[nit]** Missing docstring on `_new_helper()`. Team standard is ≥80% docstring coverage (interrogate gate, per CONTRIBUTING.md). ### Not commented on Style / naming / whitespace — those are ruff's job. Reviewed only what ruff cannot catch.
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.
- 1mo ago First seen · 172 lines · 83 tokens per session scan A b1b43b276f31
rag-code-review is a skill published in the GitHub repository lyonzin/knowledge-rag (297 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 1,766 once invoked, about $0.0003 per session on Opus 5.5. 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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