bmad-code-review

Reviews code changes with parallel independent reviewers, then triages and presents findings.

Updated Mar 14, 2026
One-click install
npx skills add https://github.com/ArchaonHW/MingGoRTS --skill bmad-code-review-archaonhw
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: bmad-code-review
Source: https://github.com/ArchaonHW/MingGoRTS/tree/main/.agents/skills/bmad-code-review
Command: npx skills add https://github.com/ArchaonHW/MingGoRTS --skill bmad-code-review-archaonhw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Code reviews often miss edge cases, verification gaps, and spec deviations because a single reviewer cannot cover every angle. This Skill runs several independent review layers in parallel against a unified diff, then verifies, triages, and routes every finding so only real, actionable issues reach the developer. ## Core Features & Use Cases - Parallel adversarial review layers: Launches independent reviewers (Blind Hunter, Edge Case Hunter, Verification Gap, Acceptance Auditor) as subagents against a staged diff file, each with its own methodology. - Evidence-based triage: Verifies every finding against the actual code, assigns verdicts (high/medium/low/false/maybe-false), groups by root cause, and routes results into decision-needed, patch, or defer buckets. - Sprint and story integration: Writes findings into story files, syncs sprint-status.yaml, and supports spec-based (full) or spec-free (no-spec) review modes. - Use Case: After finishing a feature branch, ask for a code review; the Skill diffs against the base branch, runs all review layers, rejects false positives, and applies approved patches automatically. ## Quick Start Ask the assistant to run a code review on your current branch changes against main.

Frequently Asked Questions about bmad-code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an automated code review on a git branch?

Invoke the review workflow and choose a diff source: uncommitted changes, staged changes, a branch diff against a base branch, a commit range, or a pasted diff. The Skill writes the diff to a temp file and runs parallel review layers against it.

What does a multi-layer AI code review check for?

It checks for missing edge-case handling, regressions from deleted code, falsified claims in commit messages, verification gaps where no test would catch a breakage, and deviations from the specification's acceptance criteria.

Can AI code review work without a specification file?

Yes. Declare no-spec mode explicitly and the review proceeds without a spec; the Acceptance Auditor layer is skipped and decision-needed findings are reclassified as patch or defer items.

How are false positives handled in automated code review findings?

Every finding is verified against the actual code at the cited location before triage. Claims that do not reproduce are marked false with a written refutation and rejected, so only confirmed issues reach the final report.

What happens if review subagents are not available?

The workflow writes each layer's self-contained review prompt to the implementation artifacts directory and halts, asking you to run each prompt in a separate session and paste the findings back to resume triage.