codex-review-loop

A workflow for adversarial pull-request review using the Codex command-line tool. It turns review findings into approved fixes and can repeat the review afterward.

In plain words
What is it for?
Reviewing pull requests, branches, or uncommitted changes; analyzing findings; applying approved fixes; and re-running checks.
Why use it?
It provides a structured way to find defects, require human approval, verify changes, and check whether issues remain.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/soju06/codex-lb/codex-review-loop
Any agent
npx skills add Soju06/codex-lb --skill codex-review-loop
Clone the repo
git clone --depth 1 https://github.com/Soju06/codex-lb

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,705 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.01705
Opus 5 $0.00018 $0.00852
Sonnet 5 $0.00007 $0.00341
Haiku 4.5 $0.00004 $0.00170

Measured 3d ago against content hash 10205bf38a40, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codex-review-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/codex-subagent.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/codex-review-loop/SKILL.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Codex Review Loop

Adversarial code review via Codex CLI, structured finding analysis, HITL-gated fix loop, optional re-verification.

Arguments

  • --pr <N> — review PR number N (default: auto-detect most recent open PR)
  • --base <branch> — review changes against base branch
  • --uncommitted — review uncommitted local changes

Phases

  1. Scope Resolution — determine review target (PR/branch/uncommitted)
  2. Codex Review — launch adversarial review via Codex CLI (~20-50 min)
  3. Finding Analysis — parse raw output into structured findings
  4. Atomic Fix Loop — fix, verify, commit each approved finding
  5. Re-review Loop — re-run review until 0 findings (max 3 iterations)
  6. Final Report — summary of all findings, fixes, and verification status

Phase 1: Scope Resolution

Determine the review target and extract the base branch.

  • PR mode (default): Use gh pr view / gh pr list to resolve base branch and display PR metadata (title, file count, additions/deletions).
  • Branch mode (--base): Use specified base branch directly.
  • Uncommitted mode (--uncommitted): Review working tree changes.

If no argument is given, auto-detect the user's most recent open PR and confirm.


Phase 2: Codex Review

Launch the adversarial review as a background process.

  1. Run the review script with run_in_background=true:
    bash <skill-dir>/scripts/codex-subagent.sh --base <branch>
    
    Note: Codex CLI v0.105.0+ does not support combining --base/--commit with a custom prompt. The built-in review logic is used automatically. For --uncommitted mode (no diff target), pipe stdin for custom instructions:
    cat <prompt-file> | bash <skill-dir>/scripts/codex-subagent.sh --uncommitted
    
  2. Inform the user the review is running (~20-50 min).
  3. The script parses Codex output and returns the final review text. Review rollouts intentionally remain persistent. If terminal output is lost, use codex resume --include-non-interactive to locate the review, or codex resume <SESSION_ID> when its ID is known. Do not add --ephemeral to the wrapper. The wrapper also relies on the configured non-interactive approval/sandbox policy because Codex CLI 0.147.0 removed the historical --full-auto argument from exec review.

Read the full file on GitHub · 195 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 195 lines · 36 tokens per session scan A 10205bf38a40

Subscribe to this mod's changes

codex-review-loop is a skill published in the GitHub repository Soju06/codex-lb (2,907 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,705 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

meeting-facilitate

组织多 Agent 会议全生命周期——创建会议、spawn 真实参与者、推进轮次、签到校验、结束汇总。当需要多方协作做决策、评审方案、辩论分歧、复盘项目、头脑风暴或方案评估时使用本技能。.

CronusL-1141/AI-company · 72 tokens

os-workflow

在 AI Team OS 项目里使用 CC 内置 Workflow(ultracode)时,让工作流产出回写 OS 的标准做法。当 Leader 准备调用 Workflow 工具编排子 agent 时使用。.

CronusL-1141/AI-company · 51 tokens

codexu-release

Run this skill from the repository root. Treat the release as a real public publish: verify the current remote state first, keep unrelated user changes out of release commits, and report exactly what was built, pushed, and released.

shanggqm/codexU · 63 tokens

codexu-pr-review

Review codexU pull requests and decide whether they should be merged, changed, split, declined, or kept as a fork based on product positioning, roadmap, privacy, data semantics, native macOS design, architecture, scope, and verification. Use for PR review, mergeability assessment, contribution triage, and feature-fit…

shanggqm/codexU · 79 tokens

meeting-participate

Participate in AI Team OS meetings with structured discussion rounds.

CronusL-1141/AI-company · 16 tokens

os-release

发布 AI Team OS 新版本的完整清单——预检、版本七处锁步、中英双语 CHANGELOG、双份 dist 构建、私有术语扫描、commit/tag、双仓推送、建 GitHub Release 条目并核对 latest 徽章、事后核对。当准备发版、补建漏掉的 Release 条目、或核对已发版本的线上状态时使用。.

CronusL-1141/AI-company · 94 tokens