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 rules/kunalsuri/ai-fication-kit/review-changegit clone --depth 1 https://github.com/kunalsuri/ai-fication-kitWrote 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/rules/kunalsuri/ai-fication-kit/review-change)<a href="https://agentmods.dev/rules/kunalsuri/ai-fication-kit/review-change"><img src="https://agentmods.dev/badge/rules/kunalsuri/ai-fication-kit/review-change.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 | $0.00405 |
| Opus 5 | $0.00015 | $0.00202 |
| Sonnet 5 | $0.00006 | $0.00081 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
review-change 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.
What it actually says
Review a completed change against its spec. Run this in a session that did NOT implement the change — a reviewer sharing the implementer's context inherits the implementer's blind spots.
- Pin the scope. Identify the exact diff (commits / branch / files) and the
spec or bugfix doc in
ai/lab/specs/that authorized it. No spec ⇒ that is finding #1, severity blocker: unspecced work. - Copy the template.
ai/lab/reviews/REVIEW_TEMPLATE.md→ai/lab/reviews/REVIEW_<work-id>.md. - Check with evidence, not assertions. For each check in the template — spec conformance, surgical diff, Stability respected, tests, conventions, knowledge updated, provenance clean — record where you looked and what you saw. Re-run the suites the spec names; do not trust the implementer's report.
- File findings by severity. Any blocker or major ⇒ verdict
request-changesand hand the list back to the implementer. Minor/nit findings can ship with notes. - Verdict and hand-off. Fill "what the human should double-check" — the
judgement calls a mechanical check cannot make. The review itself is
[inferred]; the human's merge decision is the real approval, and this document is its evidence. - Record. Link the review from the work's row in
ai/lab/WORKLOG.md(Review column) and set that row's Status toin-review.
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 · 29 lines · 29 tokens per session scan A e80a2a73ac93
review-change is a cursor rule published in the GitHub repository kunalsuri/ai-fication-kit (3 stars, last pushed 2d ago), licensed Apache-2.0. It adds 29 tokens to every session and 405 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 2026-08-31.
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