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 iliaal/whetstone --skill code-reviewgit clone --depth 1 https://github.com/iliaal/whetstoneWrote 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/iliaal/whetstone/code-review)<a href="https://agentmods.dev/skills/iliaal/whetstone/code-review"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/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/iliaal/whetstone/code-review"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 47 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00065 | $0.00811 |
| Opus 5.5 | $0.00026 | $0.00324 |
| Sonnet 5.5 | $0.00013 | $0.00162 |
| Haiku 4.5 | $0.00006 | $0.00081 |
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 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Two-Stage Review
Stage 1 — Spec compliance (do this FIRST): verify the changes implement what was intended. Check against the PR description, issue, or task spec. Identify missing requirements, unnecessary additions, and interpretation gaps. If the implementation is wrong, stop here — reviewing code quality on the wrong feature wastes effort.
Stage 2 — Code quality: only after Stage 1 passes, review for correctness, maintainability, security, and performance.
Review Process
- Context — read the PR description, linked issue, or task spec. Run the project's test/lint suite if available (
npm run test,make check, etc.) to catch automated failures before manual review. - Structural scan — architecture, file organization, API surface changes. Flag breaking changes.
- Line-by-line — correctness, edge cases, error handling, naming, readability. Use question-based feedback ("What happens if
inputis empty here?") instead of declarative statements to encourage author thinking. - Security — input validation, auth checks, secrets exposure, injection vectors (SQL, XSS, command). Flag race conditions (TOCTOU, check-then-act).
- Removal candidates — identify dead code, unused imports, feature-flagged code that can be cleaned up. Distinguish safe-to-delete (no references) from defer-with-plan (needs migration).
- Summary — present findings grouped by severity, then ask user how to proceed. Do NOT auto-implement fixes.
Severity Levels
- Critical — must fix before merge. Security vulnerabilities, data loss, broken functionality, race conditions.
- Important — should fix before merge. Performance issues, missing error handling, poor maintainability, silent failures.
- Minor — optional. Naming, style preferences, minor simplifications. Skip if linters already cover it.
What to Check
Correctness:
- Edge cases (null, empty, boundary values, concurrent access)
- Error paths (are failures handled or swallowed?)
- Type safety (implicit conversions,
anytypes, unchecked casts)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 77 lines · 65 tokens per session scan A 8df0fba63db6
code-review is a skill published in the GitHub repository iliaal/whetstone (35 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 811 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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pr-sweep
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