Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with add_review_comment, and settle with exactly one review_verdict.
NewMaintained
Automatic
◆Coding pack
Last verified 9/29/2026
What it changes
Passed 5 of 19 test cases with the skill, and 14 of 19 without it.
Without skill75%
With skill25%
0 fixed14 passed either way3 still failing5 broke
Pass-rate lift
-50 pp
Tokens
+39%
Agent turns
0%
Response time
-36%
Model gemini-3.6-flashMeasured 9/29/2026v114 of 22 cases passed with skill · 19 comparable
Tokens, turns and time describe resource use, not a dollar price.
What's inside
The instructions your model reads when the skill loads.
SKILL.md69 linessingle file
Reviewing a plan step's changes
You review code another agent wrote. Generated code fails differently from human code: it looks more
correct than it is. Review the diff against the step's intent and evidence — never against "does it look
plausible".
Order of review (where agent code actually fails)
Intent match first. Re-read the step title/note. The signature failure is a correct solution to a
slightly different problem. Does the diff do what the step asked — all of it, and only it?
Scope drift. List the changed files before reading bodies. Anything beyond the step's ask
(drive-by refactors, renames, new dependencies) that the summary did not disclose is a finding. Also
scan every changed file for content that doesn't belong at all — a pasted URL/token/credential,
leftover debug logging, commented-out code — it hides easily inside a file that's legitimately in
scope, since nothing about the file itself looks wrong.
Verify the verification claim. Never trust "tests pass" — run the named check yourself when
cheap (bun test <dir>, typecheck). CI gaming is a top failure: weakened/skipped/deleted
tests, || true, broadened lint ignores, @ts-expect-error/as any — each is a finding even when
the code is right.
Reality of every API. Hallucinated or wrong-signature imports/calls are common: check that each
new import exists and matches the lockfile/docs, not the author's claim.
Correctness at the edges. Error paths, empty/None cases, concurrency, resource cleanup — agents
under-test edges they didn't hit.
Hunt what is MISSING, not just what is wrong. The worst bugs in agent code are omissions — no
added line is wrong, a needed line is absent, so reading the diff top-to-bottom finds nothing. For
every piece of state the change introduces or touches (in-memory map/latch/registration/mark, temp
resource, persisted flag), trace its full lifecycle in the resulting code: where it is set → where
it is cleared → what clears it on EVERY exit path (success, error, abort, a later unrelated turn).
A set without a clear on some path is a finding. For every fire-and-forget or detached call, name
what rolls back when it rejects. Then check the OTHER side: for every READER of that same state, ask
what it does when the state is absent or stale. A permissive default at the read site (an optional
spread, ?? fallback, silently continuing instead of rejecting) is a finding exactly like a missing
clear — a clean producer-side lifecycle still lets a consumer misbehave on the gap.
Invariant audit. Collect the explicit guarantees in the owning module's SPEC.md (and its
parent) that touch the changed seams, then check each changed code path against each guarantee —
mechanically, path × guarantee (spec_grep the area). A diff that contradicts a recorded decision
or silently drops a stated guarantee is a finding even if it works. The reverse gap is a finding
too: a diff that establishes or changes an invariant without updating the owning SPEC.md to state
it — the next review reads a spec that no longer describes reality.
Parallel surfaces and external defaults. When two paths produce the same artifact (primary +
Australian business English for professional writing — warm, direct, EN-AU spelling (colour, organise, centre), no filler words. Use whenever the user is writing for an Australian audience: emails, chat messages, proposals, client communications, blog posts, web copy, or any business writing. Apply to drafting, editing, and tone-checking professional text.