Borrowing it
Nothing to install: this file belongs to orka-agents/orka. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/orka-agents/orka/main/.agents/skills/autoreview/SKILL.mdgit clone --depth 1 https://github.com/orka-agents/orkaWrote 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/orka-agents/orka/autoreview)<a href="https://agentmods.dev/skills/orka-agents/orka/autoreview"><img src="https://agentmods.dev/badge/skills/orka-agents/orka/autoreview.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 | $0.00024 | $0.07883 |
| Opus 5 | $0.00012 | $0.03941 |
| Sonnet 5 | $0.00005 | $0.01577 |
| Haiku 4.5 | $0.00002 | $0.00788 |
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 7d 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 — 456 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 max 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-opus-5 with max reasoning by default. Pi and Kimi use the model configured by their respective CLIs unless --model overrides it.
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 / Kimi review / autoreview / second-model review
- after non-trivial code edits, before final/commit/ship
- reviewing a local branch or PR branch after fixes
Do not require autoreview for a change whose entire diff is prose-only internal notes or SKILL.md documentation. Still inspect the diff directly and run the repository's lightweight documentation validation, if any. This exception does not cover user-facing documentation, executable examples, configuration, scripts, generated files, or behavior changes.
Contract
- Default output is P0 only: report issues worth blocking the current change
because they materially break the normal flow, outcome, or safety boundary.
Use
--max-priority P1,P2, orP3only when the caller explicitly asks for a wider review. - 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, unrelated rewrites, and fixes that over-complicate the codebase.
- Prefer root-cause fixes at the right ownership boundary. A coherent refactor is appropriate when it removes the bug class, duplicate policy, stale paths, or ownership confusion; do not default to a symptom patch.
- When an accepted finding exposes a bug class or repeated pattern, inspect its owner and relevant sibling implementations before fixing.
- Fix the same bug class across its owner-boundary neighborhood when practical; stop at unrelated invariants, different owners, and unapproved contract changes.
- Keep going until structured review returns no accepted/actionable findings only while the work remains inside the authorized architectural and 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.
- Before engine invocation, autoreview runs TruffleHog over temporary snapshots of the exact added, modified, or deleted content under review. It intentionally matches TruffleHog's low-false-positive pre-commit policy (
verified,unknown); it does not classify arbitrary password-like strings or rescan unchanged history. After that scan passes, locally recognized secret-like values are redacted in place only when they occur exclusively on deleted lines of an entirely removed file; if one of those deleted values also occurs in added, context, or mixed staged/unstaged content, the review fails closed. Install TruffleHog using its official platform-neutral instructions; autoreview fails with that link when the binary is unavailable and never auto-installs it. Repositories should also run TruffleHog in pull-request CI as a backup outside autoreview; repository-local Git hooks are optional. Review bundles still omit security-sensitive paths or files, and explicit prompt and dataset inputs remain checked before engine invocation. Safe large diffs are sent as one pass while they fit the aggregate prompt limit, then partitioned into complete bounded passes without truncation. - 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 validated bundle, calls the selected engine once for normal inputs or once per complete bounded chunk for oversized inputs, validates the structured results, 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
7 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.
- 7d ago First seen · 456 lines · 24 tokens per session scan A 65e65f31e048
autoreview is a skill published in the GitHub repository orka-agents/orka (22 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 7,883 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 2026-08-30.
Other skills, from other repositories
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Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
multi-reviewer-patterns
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
sage-review
Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
pr-review
Review a GitHub pull request using multiple expert personas. Takes a PR URL as input, analyzes the changes, and generates comprehensive review feedback from different perspectives (Merge Specialist, Frontend, Backend, Security, DevOps, AI/Agent, SRE, Chief Architect).
review-fix-signoff-loop
Use when writing Agent Relay or Ricky workflows that must loop review, fix, and validation with fresh agent context until independent signoff agents, typically Claude and Codex, both agree the work is comprehensively complete. Covers fresh-context iterations, repairable gates, dual reviewer verdict contracts…