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 skills/openclaw/acpx/autoreviewnpx skills add openclaw/acpx --skill autoreviewgit clone --depth 1 https://github.com/openclaw/acpxWhat 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 | $0.00020 | $0.06641 |
| Opus 5 | $0.00010 | $0.03320 |
| Sonnet 5 | $0.00004 | $0.01328 |
| Haiku 4.5 | $0.00002 | $0.00664 |
Grade A, and why
autoreview 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 3d 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.
This is a copy
88% identical to autoreview — 36 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Review
Run the bundled structured review helper as a closeout check. This is code review, not Guardian auto_review approval routing.
Codex review is the default when no engine is set. It uses gpt-5.6-sol with high reasoning by default, then retries once with gpt-5.6-terra only when the account cannot access Sol. Claude review is optional and uses claude-fable-5 by default.
For user-visible behavior, pair autoreview with behavior-validator. Autoreview is source-aware and judges the change bundle; behavior validation is source-blind and judges the running product or tool against a behavior contract. A clean autoreview is not proof that a UI, CLI, API, or generated artifact works from the user's perspective.
Use when:
- user asks for Codex review / Claude review / Pi review / autoreview / second-model review
- after non-trivial code edits, before final/commit/ship
- reviewing a local branch or PR branch after fixes
Contract
- Treat review output as advisory. Never blindly apply it.
- Verify every finding by reading the real code path and adjacent files.
- Read dependency docs/source/types when the finding depends on external behavior.
- Reject unrealistic edge cases, speculative risks, broad rewrites, and fixes that over-complicate the codebase.
- Prefer small fixes at the right ownership boundary; no refactor unless it clearly improves the bug class.
- When an accepted finding shows a bug class or repeated pattern, inspect the current PR scope for sibling instances before fixing.
- Fix the scoped bug class at once when practical; stop at touched surfaces, owner boundaries, and clear follow-up territory.
- Keep going until structured review returns no accepted/actionable findings only while the work remains inside the original task scope.
- If a review-triggered fix changes code, rerun focused tests and rerun the structured review helper.
- For security-audit suppression changes, verify accepted findings remain auditable: suppressed findings stay in structured output, active output keeps an unsuppressible suppression notice, and aggregate findings cannot hide unrelated active risk.
- Never switch or override the requested review engine/model except for the documented Codex Sol-to-Terra account-access fallback. Capacity, rate-limit, and unrelated failures keep the same engine/model.
- Be patient with large bundles. Structured review can take up to 30 minutes while the model call is active, especially with Codex tools or web search.
- Treat heartbeat lines like
review still running: ... elapsed=... pid=...as healthy progress, not a hang. Let the helper continue while heartbeats are advancing. Pass--stream-engine-outputwhen live engine text is useful; Codex and Claude filter tool/file chatter, other runnable engines pass raw output through. - Do not kill a review just because it has been quiet for 2-5 minutes, or because it is still running under the 30-minute window. Inspect the process only after missing multiple expected heartbeats, after 30 minutes, or after an obviously failed subprocess; prefer letting the same helper command finish.
- Tools are useful in review mode. Codex receives the validated bundle in an empty workspace so ignored files and linked-worktree metadata remain unreadable; web search stays available for dependency contracts and upstream docs.
- Security perspective is always included, but it should not cripple legitimate functionality. Report security findings only when the change creates a concrete, actionable risk or removes an important safety check.
- Reviewer subprocesses preserve engine authentication and non-credentialed proxy variables needed by headless or restricted-network environments while stripping process-injection, Git override, and credentialed proxy values.
- Review bundles fail closed before engine invocation when tracked or untracked paths look sensitive, patch text looks secret-like, or a Git diff exceeds the bundle limit. Redact/split the change; never accept a truncated patch as complete review proof.
- For regression provenance, keep roles separate: blamed code author, blamed PR author, PR merger/committer, current PR author, and PR/date. If no blamed PR is traceable, use the blamed commit as the provenance: commit SHA, date, and author username. Do not guess a merger or frame missing PR metadata as a separate finding.
- If the blamed PR was merged by
clawsweeper[bot]or another automation, identify the human trigger when practical. Check timeline/comments first; if rate-limited, use gitcrawl/cache or public PR HTML. Look for maintainer commands such as@clawsweeper automerge,/landpr, or labels/status comments that armed automerge. Reportautomerge triggered by @login; if not found, say trigger unknown. - Do not invoke built-in
codex review, nested reviewers, or reviewer panels from inside the review. The helper builds one bundle, calls one selected engine, validates one structured result, and stops. - Stop as soon as the helper exits 0 with no accepted/actionable findings. Do not run an extra review just to get a nicer "clean" line, a second opinion, or clearer closeout wording.
- Treat the helper's successful exit plus absence of actionable findings as the clean review result, even if the underlying Codex CLI output is terse.
- Multi-reviewer panels are opt-in only. Use them when explicitly requested or when risk justifies the extra spend; the main agent still verifies every accepted finding before fixing.
- If rejecting a finding as intentional/not worth fixing, add a brief inline code comment only when it explains a real invariant or ownership decision that future reviewers should know.
- If
gh/Gitcrawl reportsdatabase disk image is malformed, rungitcrawl doctor --jsononce to let the portable cache repair before retrying review; do not bypass the shim unless repair fails and freshness requires live GitHub. - If Gitcrawl reports a portable manifest mismatch, source/runtime DB health error, or stale portable-store checkout, run
gitcrawl doctor --jsonand inspectsource_db_health,runtime_db_health, andportable_store_statusbefore falling back to live GitHub. - Do not push just to review. Push only when the user requested push/ship/PR update.
What ships with it
8 files 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.
- 3d ago First seen · 413 lines · 20 tokens per session scan A 8d92cf5decaf
autoreview is a skill published in the GitHub repository openclaw/acpx (3,202 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 6,641 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to autoreview, differing in 36 lines, and is treated as a copy.
Other skills, from other repositories
release
Run the Harnss release workflow — review staged diff, bump version, commit, tag, push, and create a GitHub release. Use when releasing, bumping version, tagging, or creating a release. Argument: major, minor, or patch.
vercel-react-best-practices
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.