7 minute read

Automating the Cross-Model Review Loop: Three Levels from SKILL.md to Multi-AI Pipeline

The cross-model review pattern — where one AI writes code and a structurally different AI reviews it — has become a core quality practice in agentic development. Claude Code and Codex CLI have different training distributions and different blind spots, making their disagreements genuinely informative1. By late March 2026, the ecosystem offers three distinct automation tiers, each trading setup complexity for hands-off operation. This article walks through all three, with concrete configuration and the security caveats you need to understand before deploying them.

Why Cross-Model Review Works

Single-model review suffers from sycophancy bias: the same system that wrote the code tends to approve it2. Cross-provider review sidesteps this because Claude and GPT-5.x have fundamentally different failure modes. When both models flag the same issue, confidence is high. When only one flags it, that disagreement is the signal worth investigating — the “two doctors, same patient” heuristic1.

The standard execution path uses codex exec in non-interactive mode with a read-only sandbox, ensuring the reviewer cannot modify the codebase it is assessing3:

codex exec -m gpt-5.3-codex -s read-only "Review the following diff for bugs, security issues, and style violations: $(git diff HEAD~1)"

Level 1: SKILL.md — Manual Trigger, Minimal Setup

A SKILL.md file is a single Markdown document placed in .claude/skills/ that any LLM agent can parse1. This is the lowest-friction entry point: no plugins, no hooks, no external dependencies beyond a working codex binary.

Directory Structure

.claude/
  skills/
    codex-review/
      SKILL.md

The Review Loop

The SKILL.md defines a /codex-review slash command that executes a sequential fix loop:

flowchart TD
    A["/codex-review invoked"] --> B["Export current plan/diff"]
    B --> C["codex exec read-only review"]
    C --> D{"Verdict?"}
    D -->|PASS| E["Review complete"]
    D -->|CONCERNS| F["Claude addresses findings"]
    F --> G{"Round < 5?"}
    G -->|Yes| C
    G -->|No| H["Escalate to human"]

Each round uses a UUID-bound session ID for concurrency safety, and the review runs under --sandbox read-only to enforce immutability1. The key codex exec invocations:

# Initial review
codex exec -m gpt-5.3-codex -s read-only \
  "Review this plan against the codebase. Respond PASS or CONCERNS with details."

# Re-review after fixes (resume session for context continuity)
codex exec resume <session-id> \
  "Re-review the updated plan. Previous concerns were: ..."

Level 1.5: Fresh-Session Audit

A refinement worth adopting early: after the fix loop converges, spawn a fresh Codex session for a final audit1. This eliminates context bias from the iterative conversation and catches systemic issues the loop might have normalised. The audit uses a distinct verdict format — AUDIT: PASS or AUDIT: CONCERNS — to differentiate it from loop rounds.

When to use Level 1: Solo developers or small teams wanting to validate the cross-model approach before investing in automation infrastructure. Setup time is under five minutes.

Level 2: Stop Hook Plugins — Automatic Trigger

Level 2 eliminates the manual /codex-review invocation by hooking into Codex CLI’s lifecycle system. When Claude Code attempts to complete a turn, a Stop hook intercepts the exit and triggers a Codex review automatically4.

How Codex Hooks Work

Hooks are defined in hooks.json at user level (~/.codex/hooks.json) or repository level (<repo>/.codex/hooks.json)5. The Stop hook fires at conversation turn completion:

{
  "hooks": {
    "Stop": [
      {
        "hooks": [
          {
            "type": "command",
            "command": ".claude-plugin/hooks/stop-hook.sh",
            "statusMessage": "Running cross-model review...",
            "timeout": 900
          }
        ]
      }
    ]
  }
}

The hook communicates its decision via exit codes5:

  • Exit 0 with JSON {"decision": "block", "reason": "..."} — blocks the stop, feeds the reason back as a continuation prompt
  • Exit 0 without blocking JSON — permits the stop
  • Exit 2 — blocks; reads reason from stderr

Option A: codex-plugin-cc (Official)

OpenAI released codex-plugin-cc on 30 March 20266, providing a single-command review gate:

# Install
/plugin marketplace add openai/codex-plugin-cc
/plugin install codex@openai-codex
/codex:setup

# Enable automatic review gate
/codex:setup --enable-review-gate

When enabled, every Claude Code turn completion triggers a targeted Codex review. If issues are found, the stop is blocked and Claude addresses the findings before the turn can end6. The plugin also exposes manual commands:

Command Purpose
/codex:review --base main Diff review against a branch
/codex:adversarial-review Devil’s advocate design challenge
/codex:rescue --background Delegate a task to Codex asynchronously

⚠️ Cost warning: The review gate can create long-running loops that rapidly consume usage limits. OpenAI’s own documentation recommends enabling it only under human supervision6.

Option B: claude-review-loop (Community)

The claude-review-loop plugin by Hamel Husain takes a more opinionated approach, spawning up to four parallel Codex sub-agents based on project type7:

Sub-Agent Trigger Focus
Diff Review Always Code quality, tests, OWASP Top 10
Holistic Review Always Architecture, documentation
Next.js Review next.config.* present App Router, Server Components, caching
UX Review Frontend code detected Browser E2E via agent-browser, accessibility 
# Install
/plugin marketplace add hamelsmu/claude-review-loop
/plugin install review-loop@hamel-review

Codex deduplicates findings across agents and writes consolidated output to reviews/review-<id>.md7. State is tracked in .claude/review-loop.local.md (gitignored).

Security: The bypass-sandbox Default

Both community plugins default to --dangerously-bypass-approvals-and-sandbox for Codex execution7. This is necessary because the review agents need file-system read access, but it means Codex runs without sandbox constraints. Override this with:

export REVIEW_LOOP_CODEX_FLAGS="--sandbox read-only"

For codex-plugin-cc, the official plugin uses the Codex app server which applies its own sandbox policy, making this less of a concern6.

Preventing Infinite Loops

A critical implementation detail: your stop hook must check a stop_hook_active flag before spawning another review1. Without this guard, the review’s own completion triggers another stop hook, creating an infinite loop:

#!/bin/bash
STATE_FILE=".claude/review-loop.local.md"
if grep -q "stop_hook_active: true" "$STATE_FILE" 2>/dev/null; then
  exit 0  # Permit stop — we're already in a review cycle
fi

Level 3: Multi-AI Pipeline Governance

Level 3 moves beyond a single reviewer to orchestrated multi-model pipelines where different AI systems handle distinct quality dimensions.

claude-codex: Sequential Review Chain

The claude-codex plugin (Z-M-Huang) implements a three-reviewer pipeline8:

flowchart LR
    A["Implementation\n(Claude Sonnet)"] --> B["Review 1\n(Claude Sonnet)"]
    B --> C["Review 2\n(Claude Opus)"]
    C --> D["Final Gate\n(Codex CLI)"]
    D -->|Pass| E["Approved"]
    D -->|Fail| F["Fix + Re-review"]

Each reviewer independently validates against OWASP Top 10 vulnerabilities8. The pipeline enforces sequential dependencies via blockedBy constraints — Review 2 cannot start until Review 1 approves. If any reviewer requests changes, a fix task and re-review are automatically created.

# Feature development with full pipeline
/claude-codex:multi-ai Add rate limiting to the authentication endpoint

# Bug fix with dual root-cause analysis
/claude-codex:bug-fix Session tokens not invalidated on password change

Configuration controls iteration limits8:

  • Plan review loop: 10 iterations maximum
  • Code review loop: 15 iterations maximum
  • Auto-resolve attempts: 3 retries before pausing for human input

⚠️ Note: This repository was archived on 22 February 2026; development continues at Z-M-Huang/vcp/plugins/dev-buddy8.

GitHub Agent HQ: Platform-Level Integration

GitHub’s Agent HQ, in public preview since February 2026, achieves platform-level cross-model integration1. From a single issue, you can launch Copilot, Claude Code, and Codex agents simultaneously, comparing their outputs. This requires Copilot Pro+ or Enterprise licensing.

Mapping to Agentic Pod Roles

The three levels map naturally to agentic pod structures1:

Level Pod Role Equivalent Team Size
Level 1 (SKILL.md) Solo developer self-review 1–2
Level 2 (Stop Hook) Quality Engineer in the loop 3–8
Level 3 (Pipeline) Full pod with dedicated QA 8+

Choosing Your Level

flowchart TD
    A["Starting cross-model review?"] --> B{"Team size?"}
    B -->|"Solo / pair"| C["Level 1: SKILL.md\n5 min setup"]
    B -->|"Small team"| D{"Want automatic triggers?"}
    D -->|Yes| E["Level 2: Stop Hook\ncodex-plugin-cc or\nclaude-review-loop"]
    D -->|No| C
    B -->|"Large team / enterprise"| F["Level 3: Pipeline\nclaude-codex or\nGitHub Agent HQ"]
    E --> G{"Need multi-reviewer?"}
    G -->|Yes| F
    G -->|No| E

Start with Level 1 to validate that cross-model review catches real issues in your codebase. Promote to Level 2 when you find yourself routinely forgetting to invoke the review. Graduate to Level 3 when your team needs formalised quality gates with audit trails.

Practical Recommendations

  1. Always enforce read-only sandbox for review agents. A reviewer that can modify code is a reviewer that can mask its own findings.
  2. Set explicit timeouts. The default 900-second timeout for stop hooks is generous; most reviews complete in under 60 seconds. Reduce to 120 seconds to fail fast on stuck sessions.
  3. Monitor token consumption. Level 2 and 3 multiply your API usage significantly. Use --model gpt-5.4-mini for routine reviews and reserve full models for adversarial passes6.
  4. Git-ignore review state files. Both .claude/review-loop.local.md and .task/ directories contain transient state that should not enter version control.
  5. Pin your reviewer model. Use explicit model identifiers in configuration rather than aliases to avoid unexpected behaviour when model defaults change.

Citations

  1. SmartScope, “Automating the Claude Code × Codex Review Loop — Three Levels,” March 2026. https://smartscope.blog/en/blog/claude-code-codex-review-loop-automation-2026/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8

  2. MindStudio, “What Is the OpenAI Codex Plugin for Claude Code? How Cross-Provider AI Review Works,” 2026. https://www.mindstudio.ai/blog/openai-codex-plugin-claude-code-cross-provider-review ↩

  3. OpenAI, “Agent approvals & security – Codex,” 2026. https://developers.openai.com/codex/agent-approvals-security ↩

  4. OpenAI, “Introducing Codex Plugin for Claude Code,” OpenAI Developer Community, March 2026. https://community.openai.com/t/introducing-codex-plugin-for-claude-code/1378186 ↩

  5. OpenAI, “Hooks – Codex,” OpenAI Developers, 2026. https://developers.openai.com/codex/hooks ↩ ↩2

  6. OpenAI, “codex-plugin-cc,” GitHub, March 2026. https://github.com/openai/codex-plugin-cc ↩ ↩2 ↩3 ↩4 ↩5

  7. Hamel Husain, “claude-review-loop,” GitHub, 2026. https://github.com/hamelsmu/claude-review-loop ↩ ↩2 ↩3

  8. Z-M-Huang, “claude-codex: Multi-AI orchestration plugin,” GitHub, 2026. https://github.com/Z-M-Huang/claude-codex ↩ ↩2 ↩3 ↩4

Updated:

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