wf-review-code

Reviews a diff for correctness, tests, and conventions, producing a verdict with file:line findings.

Updated May 9, 2026
One-click install
npx skills add https://github.com/23min/aiwf --skill wf-review-code-23min
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: wf-review-code
Source: https://github.com/23min/aiwf/tree/main/internal/skills/embedded-rituals/plugins/wf-rituals/skills/wf-review-code
Command: npx skills add https://github.com/23min/aiwf --skill wf-review-code-23min

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Code review quality depends heavily on who reviews and how they are briefed: self-review misses the author's own blind spots, and a bare "review this" prompt yields shallow passes. This Skill provides a structured, adversarial review checklist that classifies findings by kind and urgency so nothing actionable is silently dropped. ## Core Features & Use Cases - Structured diff walkthrough: Checks correctness, edge cases, error handling, project constraints, tests (including a manual branch-coverage audit), conventions, and documentation hygiene. - Two-axis finding classification: Every finding is labeled defect vs. judgment and blocking vs. track-for-later vs. non-issue, each with a file:line reference and a disposition (pin with a test, record a decision, or decline). - Verdict output: Produces approve, request-changes, or questions with a standardized Markdown report format. - Use Case: Before merging a feature branch, dispatch a fresh agent with this Skill and an adversarial brief listing the change's load-bearing claims; the reviewer verifies each claim by measurement and returns blocking findings with locations. ## Quick Start Ask the assistant to review the current branch diff against the stated goal using the wf-review-code checklist and return a verdict with classified findings.

Frequently Asked Questions about wf-review-code

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

FAQPage Schema
How do I review a pull request with an AI assistant?▼

Hand the reviewer the diff plus the stated goal or acceptance criteria, then walk each changed file for correctness, tests, and conventions. This Skill structures that pass and outputs a verdict of approve, request-changes, or questions with file:line findings.

How to classify code review findings as blocking or non-blocking?▼

Classify each finding on two axes: kind (defect verifiable against a test or spec, versus judgment as a design preference) and urgency (blocking before merge, track for later, or non-issue). Blocking defects must be fixed and pinned with a check.

Why is self-review of your own code unreliable?▼

The same blind spots that produced a defect shape the author's review, so self-review reliably misses what the author did not know to look for. A fresh agent given an adversarial brief and instructed to verify by measurement finds more issues.

Does this code review skill run automated coverage tools?▼

No. Branch coverage is an agent-performed manual walk of every reachable conditional branch in the diff, because typical mechanical coverage gates are statement-level. The reviewer must perform the walk rather than rely on a tool.

When should I use a codebase health check instead of a diff review?▼

Use a per-diff review for the change itself; use a codebase-health rubric for structural properties a single diff cannot reach, such as module boundaries and observability. On large or boundary-introducing diffs, run both.