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/review

Evaluate finished work for defects, risk, and simplification before it ships

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oh-my-claudecode
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Claude Code

$ npx -y skills add Yeachan-Heo/oh-my-claudecode --skill review --agent claude-code

SKILL.md

review.SKILL.md
name: omc-review
description: Evaluate finished work for defects, risk, and simplification before it ships

Review

Use this skill to evaluate work that already exists. Review never authors the change it is judging.

This is the canonical review workflow. `ai-slop-cleaner` is an opt-in lane within it.

Goal

Find what is actually wrong, ranked by severity, with enough detail to act on.

Workflow

1. Establish what changed and what it was meant to do. 2. Read the change against that intent. 3. Check correctness first, then risk, then simplification. 4. Verify each candidate finding before reporting it. 5. Report findings most-severe first.

What to check

  • **Correctness** — logic defects, edge cases, error paths, concurrency
  • **Risk** — security boundaries, destructive operations, data integrity
  • **Reuse** — existing utilities or patterns the change should have used
  • **Simplification** — code that could be deleted or collapsed
  • **Coverage** — behavior that ships untested

Two axes, run apart

A substantial diff is reviewed as two parallel sub-agent passes, reported separately — never merged or cross-ranked, because a change can pass one axis and fail the other (standards-conforming but wrong behavior; faithful but convention-breaking):

  • **Standards axis** — the diff against the repo's documented standards plus a judgement-call-only smell baseline; anything tooling already enforces is skipped. The standards set is discovered, not assumed: search the repo for standards documents (CLAUDE.md/AGENTS.md, `docs/standards/` volumes, `CONTRIBUTING.md`, `CODING_STANDARDS.md` or equivalents) and review against whatever exists — a standards file you didn't look for is a standard you didn't apply.
  • **Intent axis** — the diff against what the work was meant to do — the task, spec, or ticket acceptance criteria: requirements missing or partial, behavior nobody asked for, each finding quoting its source line.

Keep each sub-agent brief under ~400 words and pointed at one axis. Aggregate without picking a single winner across axes — that reranking is what the separation exists to prevent; state each axis's verdict on its own. A small diff stays one pass.

Rules

  • Separate lanes: the reviewer must not be the author's same active context.
  • Verify before reporting. A plausible-sounding finding that does not reproduce is noise.
  • State severity honestly; do not pad the list to look thorough.
  • "No findings" is a valid result when the work is sound.
  • Advisory by default — review informs, it does not gate. Hard gates (release,

security, destructive operations) stay separate and fail closed.

Output

  • Findings, most-severe first, each with file, line, and concrete failure scenario
  • What was checked and found clean
  • Anything that could not be assessed

Quality Gates

Before finalizing the review, check the existing merged OMC `qualityGates["omc-review"]` entries (project stage entries replace the user list; see `docs/quality-gates-configuration.md`). If there are no entries, do not call `omc quality-gates list`, spawn gate tasks, or add a Quality Gates section or gate records to the review output. If there is at least one entry, call `omc quality-gates list --stage omc-review --json` and show every validation error. For each available gate, spawn one independent Claude Code `Task` that loads the named skill from `resolvedPath`, reviews the same changes, and returns `VERDICT: PASS` or `VERDICT: BLOCK` plus findings. Use the configured model override when present; do not pass another reviewer's output to the gate. Reuse the same gate lane if the change is revised and reviewed again.

When gates are configured, record `{ gate, mode, verdict }` for each gate in a Quality Gates section of the review output. Unresolved skills are `gate_unavailable` with a visible warning, never PASS; they do not fail the review stage. A blocking `BLOCK` makes the overall result `REQUEST CHANGES`; advisory results are informational and never block.

Read more
@yeachan-heo
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For Codex users: Check out oh-my-codex — the same orchestration experience for OpenAI Codex CLI. Liked OmC but found it a bit overkill? Try gajae-code.

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Questions about review.

Which coding agents can use review?
review installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.