ai-code-review-catalog
Skill3 starsChanged 17 days agoMITView on GitHub
available code reviewers, review prompts and model routing
In this repository15 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-browser-diffSkill
- claude-parallel-writersSkill
- cross-harness-sub-agentSkill
- helper-sub-agentsSkill
- llm-evaluationSkill
- loopSkill
- orchestrate-multi-pr-workSkill
- planSkill
- propose-refactorSkill
- prSkill
- record-videoSkill
- triaging-feedbackSkill
- upgrade-dependenciesSkill
- writing-skillsSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
when using exact-commit verification evidence, run from a clean, committed checkout.
- Very specific: line 21
when the parent harness is sandboxed, launch the helper outside that parent sandbox with an escalated shell command.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
<reviewers>/cross-harness-sub-agentwith a short broad review prompt written by the main agent- native sub-agent reading and following /thermo-nuclear-code-quality-review
- native sub-agent using
prompts/ponytail-review.md - native sub-agent using
prompts/code-review.md - harness's native code review agent
codexcli native review through the shared launcher:bin/codex-native-review origin/develop # optional evidence and output location: bin/codex-native-review origin/develop playground/review-handoffs playground/checks.md- when using exact-commit verification evidence, run from a clean, committed checkout; output is a fresh directory containing
review.mdandreview.log - when the parent harness is sandboxed, launch the helper outside that parent sandbox with an escalated shell command; the reviewer itself remains
--sandbox read-only - the helper supplies source-inspection instructions through
developer_instructions, sincecodex review --baserejects a positional prompt - verification belongs to the caller or CI. supply existing evidence with its exact commit, commands or CI links, scope and pass/fail/pending status; the reviewer reports gaps instead of running write-dependent checks
- retain the report and log on failure; launcher permission failures need the correct parent launch context, not another identical attempt
- when using exact-commit verification evidence, run from a clean, committed checkout; output is a fresh directory containing
</reviewers><prompts>- code review prompt:
prompts/code-review.md - ponytail review prompt:
prompts/ponytail-review.md - bug-class review prompt:
prompts/bug-class-review.md - data leak review prompt: /data-leak-review
</prompts>native sub-agent model routing:
| harness | model | reasoning |
| codex | gpt-6.1-sol | high |
| claude code | claude-opus-5-5 | high |
| other | what the harness recommends | what the harness recommends |
don't use fast mode if a command fails, try similar variants if a cli tool is not installed/authenticated, skip it
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

