three.js model, downloadable as OBJ or GLB
日本語の概要は準備中です。原文の説明を表示しています。
Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low|medium|high|xhigh|max: from few high-confidence findings up to many, some of them uncertain; ultra: deep multi-agent review in the cloud); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. Pass --max-findings <n> to report up to n findings, or --max-findings all for every finding. The choice stays until you pass --max-findings default. For ultra on a GitHub.com PR target, --post asks to post the finished review’s findings to the PR as a single comment from the user’s GitHub account (not a review; the launch dialog still confirms in interactive sessions, while non-interactive mode posts on the flag alone) and --no-post hides that option.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
high effort → 8 inline angles → dedup (no verify) → ≤10 findings
You are reviewing for recall at high effort: catch every real bug a careful reviewer would catch in one sitting. At this level, catching real bugs matters more than avoiding false positives. Err on the side of surfacing.
Run git diff @{upstream}...HEAD (or git diff main...HEAD / git diff HEAD~1
if there's no upstream) to get the unified diff under review. If there are
uncommitted changes, or the range diff is empty, also run git diff HEAD and
include the working-tree changes in scope — the review often runs before the
commit. If a PR number, branch name, or file path was passed as an argument,
review that target instead. Treat this diff as the review scope.
Run 8 independent finder angles in sequence yourself, in THIS context — do NOT spawn subagents for them. Each
surfaces up to 6 candidate findings with file, line, a one-line
summary, and a concrete failure_scenario.
Read every hunk in the diff, line by line. Then Read the enclosing function for
each hunk — bugs in unchanged lines of a touched function are in scope (the PR
re-exposes or fails to fix them). For every line ask: what input, state, timing,
or platform makes this line wrong? Look for inverted/wrong conditions,
off-by-one, null/undefined deref, missing await, falsy-zero checks,
wrong-variable copy-paste, error swallowed in catch, unescaped regex metachars.
For every line the diff DELETES or replaces, name the invariant or behavior it enforced, then search the new code for where that invariant is re-established. If you can't find it, that's a candidate: a removed guard, a dropped error path, a narrowed validation, a deleted test that was covering a real case.
For each function the diff changes, find its callers (Grep for the symbol) and check whether the change breaks any call site: a new precondition, a changed return shape, a new exception, a timing/ordering dependency. Also check callees: does a parallel change in the same PR make a call unsafe?
The angles above hunt for bugs; this one and the next two hunt for cleanup in the changed code. Flag new code that re-implements something the codebase already has — Grep shared/utility modules and files adjacent to the change, and name the existing helper to call instead.
Flag unnecessary complexity the diff adds: redundant or derivable state, copy-paste with slight variation, deep nesting, dead code left behind. Name the simpler form that does the same job.
Flag wasted work the diff introduces: redundant computation or repeated I/O, independent operations run sequentially, blocking work added to startup or hot paths. Also flag long-lived objects built from closures or captured environments — they keep the entire enclosing scope alive for the object's lifetime (a memory leak when that scope holds large values); prefer a class/struct that copies only the fields it needs. Name the cheaper alternative.
Check that each change fixes the root cause at the right depth rather than patching a symptom with a fragile bandaid. Special cases layered on shared infrastructure are a sign the fix isn't deep enough — prefer the simpler, more general change to the underlying mechanism over adding special cases, and name that change.
Find the CLAUDE.md files that govern the changed code: the user-level ~/.claude/CLAUDE.md, the repo-root CLAUDE.md, plus any CLAUDE.md or CLAUDE.local.md in a directory that is an ancestor of a changed file (a directory's CLAUDE.md only applies to files at or below it). Read each one that exists, then check the diff for clear violations of the rules they state.
Only flag a violation when you can quote the exact rule and the exact line that breaks it — no style preferences, no vague "spirit of the doc" inferences. In the finding, name the CLAUDE.md path and quote the rule so the report can cite it. If no CLAUDE.md applies, return nothing for this angle.
Cleanup, altitude, and conventions candidates use the same
file/line/summary shape; in failure_scenario, state the concrete
cost (what is duplicated, wasted, harder to maintain, or which CLAUDE.md rule
is broken) instead of a crash. Correctness bugs always outrank cleanup,
altitude, and conventions findings when the output cap forces a cut.
Pass every candidate with a nameable failure scenario through — finders that silently drop half-believed candidates are the dominant cause of misses.
Pool all candidates. Dedup near-duplicates only (same defect, same location, same reason → keep one). Do NOT run verifiers; do NOT re-judge. Sort by severity.
Target at least 5 findings. If fewer genuine findings exist, emit what you have — do not invent to hit the floor.
Return findings as a JSON array of at most 10 objects:
[
{
"file": "path/to/file.ext",
"line": 123,
"summary": "one-sentence statement of the bug",
"failure_scenario": "concrete inputs/state → wrong output/crash"
}
]
Ranked most-severe first. If more than 10 survive, keep the 10 most
severe. If nothing survives, return []. Do not call the
ReportFindings tool even if it is available - this review's
output contract is the JSON block above.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
three.js model, downloadable as OBJ or GLB
日本語の概要は準備中です。原文の説明を表示しています。
Manage your private to-do list for the user. Use to add or update tasks, record what they are waiting on, track blockers, mark tasks complete or canceled, and clean up the list. Not for Dreamers or external task trackers.
日本語の概要は準備中です。原文の説明を表示しています。
Run a goal as a project: you coordinate. Use for "agents <task>", "take this to done", "work on this in parallel", "what are my threads doing", "pick up <slug>", "resume <slug>", "keep going until it is merged". Under `/agents` you do the work yourself unless it needs several lanes or a long wait — you judge; the plan line says why. No target you could verify (a file, API, number or measure) → the grill interview first, not threads. Read this skill before proposing anything for `/agents`: without it a proposal is in-session subagents.
日本語の概要は準備中です。原文の説明を表示しています。
Timeline-based motion design
日本語の概要は準備中です。原文の説明を表示しています。
The user's synced Apple Health (HealthKit) data: daily metrics (steps, distance, calories, heart rate, HRV, VO2max), sleep sessions (stages, quality, efficiency), and workouts.
日本語の概要は準備中です。原文の説明を表示しています。
Create, read, edit, or manipulate Word documents (.docx) and Word templates (.dotx). Use whenever a build task's artifact kind is document with the default docx output, or the task mentions a Word doc, .docx, or .dotx, extracts or reorganizes content from one, inserts or replaces images, does find-and-replace in one, or works with tracked changes (redlines) or comments. Covers python-docx generation, raw OOXML editing of existing files, document structure and formatting, and render verification. Not for PDFs, spreadsheets, or Google Docs.
日本語の概要は準備中です。原文の説明を表示しています。