Agent skill
code-review
Run `codex review` plus PAL `codereview` (via the `pal-mcporter` skill) against a git base ref, then merge both outputs into one prioritized, actionable review.
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Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/code-review-prateek-dotfiles
SKILL.md
Code Review
When to use
Use when you have a local git repo and a base ref to diff against (e.g. upstream/main, origin/master, or a commit SHA) and you want a single merged review from:
codex review- PAL
codereview(run viapal-mcporter)
Inputs
path: repo root directory (prefer absolute)compare_to: git ref to compare against (e.g.upstream/main)extra(optional): focus areas (perf, security, tests, API, etc.)
Workflow
1) Ensure the base ref exists locally
cd <path>- If
compare_tois a remote ref likeupstream/main, ensure it’s present:git fetch <remote> <branch>
2) Run Codex review + PAL codereview (parallel)
These two passes are independent once compare_to exists locally, so run them in parallel to reduce wall-clock time.
- Codex review (capture as
codex_review):cd <path> && codex review --base "<compare_to>"- Note: the current
codex reviewCLI does not accept a custom prompt when--baseis used; run it without a prompt.
- PAL review via MCPorter (capture as
pal_review):bash "<path-to-pal-mcporter-skill>/scripts/pal" -o markdown codereview --step "Review changes vs <compare_to>. <extra>" --step-number 1 --total-steps 1 --next-step-required false --findings "" --model auto --review-validation-type internal --review-type quick --severity-filter all --focus-on "<extra>"
Implementation note (when tool-parallelism is available): use multi_tool_use.parallel to run two functions.exec_command calls concurrently (one for codex review, one for pal ... codereview).
3) Merge feedback into one review
- Deduplicate overlapping findings; reconcile disagreements (call them out explicitly).
- Prioritize into:
- Blockers (must fix): correctness, security, data loss, breaking API/ABI, missing tests, CI failures.
- High-signal improvements: maintainability, performance, edge cases, observability.
- Nits: style/consistency (only if low-noise).
- End with a short verification checklist (tests to run, manual steps, rollout risk).
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