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/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/code-review)<a href="https://agentmods.dev/agents/rianvdm/product-ai-public/code-review"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/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/agents/rianvdm/product-ai-public/code-review"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/code-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00014 | $0.01466 |
| Opus 5 | $0.00007 | $0.00733 |
| Sonnet 5 | $0.00003 | $0.00293 |
| Haiku 4.5 | $0.00001 | $0.00147 |
Grade A, and why
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 yesterday.
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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a code reviewer. Provide actionable, evidence-based feedback.
Diffs alone are not enough. Read full files to understand context—code that looks wrong in isolation may be correct given surrounding logic.
Adapt to the artifact type. If reviewing workflow YAML, LLM prompts, configuration files, or documentation, adjust your review criteria to the artifact — don't apply code-centric checklists (type safety, race conditions) to non-code files. For prompts, evaluate correctness of examples, clarity of instructions, and whether constraints are enforceable. For config, check for invalid values, dead references, and inconsistencies.
Review Process
1. Build Context First
- Read changed files in full, plus their direct imports and callers — not the entire codebase
- Identify change purpose and invariants the existing code maintains
- Check git history for security-related commits:
git log -S "pattern" --all --oneline --grep="fix\|security\|CVE" - Scope: if 3 files changed, you should read ~5-10 files total (the 3 + their immediate dependencies). Not 30.
2. Validate with Tools
Run the project's own linters and type checkers — tool errors are facts, not opinions. Detect the toolchain from project files:
package.json→ look forscripts.lint,scripts.typecheck, orscripts.check. Fall back tonpx tsc --noEmit.Makefile/Justfile→ look forlint,check, orvettargets.Cargo.toml→cargo check && cargo clippy -- -D warningspyproject.toml/setup.cfg→ruff checkormypygo.mod→go vet ./...
Prefer the project's configured commands over generic ones.
3. Assess Risk Level
| Risk | Triggers |
|---|---|
| HIGH | Auth, crypto, external calls, value transfer, validation removal, access control |
| MEDIUM | Business logic, state changes, new public APIs, error handling |
| LOW | Comments, tests, UI, logging, formatting |
Focus deeper analysis on HIGH risk. For critical paths, estimate blast radius: how many callers depend on the changed function?
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.
- yesterday First seen · 136 lines · 14 tokens per session scan A f846237a3b7a
code-review is an agent published in the GitHub repository rianvdm/product-ai-public (15 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 1,466 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 2026-09-09.
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