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Run a lead code review on the current branch or a given PR. Standalone entry point for the lead-reviewer agent.
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Baseado na classificação ocupacional SOC
Generate a bespoke issue-workflow for any target project. Phase 1 — a Claude Opus analyst reads the project deeply and produces a disposition report. Phase 2 — on approval, parallel writer agents transplant every Maestro workflow component, adapted to the target project's actual stack, test runner, dev environment, and conventions. Use when a project needs its own self-contained issue-workflow without taking a direct dependency on Maestro.
Show the Maestro command map — a quick reference to all available skills.
Adversarially review a grooming spec before implementation starts. Finds hidden risks, unvalidated assumptions, and missing dependencies. Standalone entry point for the challenger agent.
Run QA validation on a pull request — boots the local environment, tests acceptance criteria, and optionally posts the report as a PR comment. Standalone entry point for the qa-engineer agent.
Groom a single GitHub issue — produce an implementation spec and optionally post the grooming summary as a GitHub comment. Standalone entry point for the grooming-agent.
Check a change against WordPress.org plugin rules and PHPCS standards.
| name | maestro:review |
| description | Run a lead code review on the current branch or a given PR. Standalone entry point for the lead-reviewer agent. |
| argument-hint | ["PR-number-or-URL"] |
Standalone code review for any PR. Runs the full lead-reviewer analysis against the spec and project standards. Posting inline comments and the summary to GitHub is your choice — you are prompted at the end.
If $ARGUMENTS is provided, use it as the PR number or URL.
Otherwise resolve from the current branch:
gh pr list --head "$(git branch --show-current)" --json number,url -q '.[0] | "\(.number) \(.url)"'
If no PR is found, tell the user and stop.
Read .claude/maestro.json. Extract:
TEMP_ROOT = .ai.temp_rootREPO = .ai.repoThen gather from the PR:
gh pr view <PR_NUMBER> --json baseRefName,body -q '{base: .baseRefName, body: .body}'
Extract the base branch and the linked issue number (look for Fixes #N, Closes #N, or
a GitHub issue URL in the PR body).
If a linked issue number was found, check for a spec at:
{TEMP_ROOT}/issues/<N>/spec.md
If the spec exists, pass its path to the agent. If it does not exist, inform the user: "No grooming spec found — the review will check against project standards only (spec compliance section will be skipped)."
Invoke the lead-reviewer sub-agent with:
CURRENT_MODEL: "standalone"session_learnings: read Section 13 of AGENTS.md if it exists, else pass empty stringSTANDALONE MODE — two differences from the normal pipeline run:
- Skip Step 5 (inline PR comments) and Step 5b (summary PR comment). Instead, output the full review report — findings table, blockers, nice-to-haves — as formatted Markdown in your response, in a section titled
## Review Report.- Skip Step 6 (StructuredOutput JSON). Return a short human-readable verdict summary instead: overall verdict, blocker count, and any open questions.
After the agent responds, display its ## Review Report and ask:
Post this review to PR #<PR_NUMBER>? Reply
yesto post inline comments + summary,noto finish here.
If yes — the agent posts inline comments (Step 5) and the summary comment (Step 5b)
using the normal dedup flow. Both respect the <!-- ai-pipeline:lead-review --> marker so
a re-run later (via the pipeline) will update in place rather than duplicate.
If no — confirm the review is complete and finish.