CO

codex-claude-loop

Automates a collaborative feedback loop between Claude Code and Codex to ensure high-quality code and architecture.

Install

mkdir -p .claude/skills/codex-claude-loop && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/181" && unzip -o skill.zip -d .claude/skills/codex-claude-loop && rm skill.zip

Installs to .claude/skills/codex-claude-loop

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Orchestrates a dual-AI engineering loop where Claude Code plans and implements, while Codex validates and reviews, with continuous feedback for optimal code quality
164 charsno explicit “when” trigger
Advanced

Key capabilities

  • Orchestrate multi-model validation loops
  • Generate cross-model feedback reports
  • Execute iterative refinement based on Codex reviews
  • Apply security and architectural linting to implementation
  • Document assumptions and edge cases

How it works

Manages a handoff cycle where one agent generates architecture/code and the second agent performs validation checks.

Inputs & outputs

You give it
Implementation plan or code snippet
You get back
Validated code and review report

When to use codex-claude-loop

  • Validating complex architectural plans
  • Reviewing code for security gaps
  • Debugging complex implementation steps
  • Ensuring high-quality output on critical tasks

About this skill

Codex-Claude Engineering Loop Skill

Core Workflow Philosophy

This skill implements a balanced engineering loop:

  • Claude Code: Architecture, planning, and execution
  • Codex: Validation and code review
  • Continuous Review: Each AI reviews the other's work
  • Context Handoff: Always continue with whoever last cleaned up

Phase 1: Planning with Claude Code

  1. Start by creating a detailed plan for the task
  2. Break down the implementation into clear steps
  3. Document assumptions and potential issues
  4. Output the plan in a structured format

Phase 2: Plan Validation with Codex

  1. Ask user (via AskUserQuestion):
    • Model: gpt-5 or gpt-5-codex
    • Reasoning effort: low, medium, or high
  2. Send the plan to Codex for validation:
   echo "Review this implementation plan and identify any issues:
   [Claude's plan here]
   
   Check for:
   - Logic errors
   - Missing edge cases
   - Architecture flaws
   - Security concerns" | codex exec -m  --config model_reasoning_effort="" --sandbox read-only
  1. Capture Codex's feedback

Phase 3: Feedback Loop

If Codex finds issues:

  1. Summarize Codex's concerns to the user
  2. Refine the plan based on feedback
  3. Ask user (via AskUserQuestion): "Should I revise the plan and re-validate, or proceed with fixes?"
  4. Repeat Phase 2 if needed

Phase 4: Execution

Once the plan is validated:

  1. Claude implements the code using available tools (Edit, Write, Read, etc.)
  2. Break down implementation into manageable steps
  3. Execute each step carefully with proper error handling
  4. Document what was implemented

Phase 5: Cross-Review After Changes

After every change:

  1. Send Claude's implementation to Codex for review:
    • Bug detection
    • Performance issues
    • Best practices validation
    • Security vulnerabilities
  2. Claude analyzes Codex's feedback and decides:
    • Apply fixes immediately if issues are critical
    • Discuss with user if architectural changes needed
    • Document decisions made

Phase 6: Iterative Improvement

  1. After Codex review, Claude applies necessary fixes
  2. For significant changes, send back to Codex for re-validation
  3. Continue the loop until code quality standards are met
  4. Use codex exec resume --last to continue validation sessions:
   echo "Review the updated implementation" | codex exec resume --last

Note: Resume inherits all settings (model, reasoning, sandbox) from original session

Recovery When Issues Are Found

When Codex identifies problems:

  1. Claude analyzes the root cause
  2. Implements fixes using available tools
  3. Sends updated code back to Codex for verification
  4. Repeats until validation passes

When implementation errors occur:

  1. Claude reviews the error/issue
  2. Adjusts implementation strategy
  3. Re-validates with Codex before proceeding

Best Practices

  • Always validate plans before execution
  • Never skip cross-review after changes
  • Maintain clear handoff between AIs
  • Document who did what for context
  • Use resume to preserve session state

Command Reference

PhaseCommand PatternPurpose
Validate planecho "plan" | codex exec --sandbox read-onlyCheck logic before coding
ImplementClaude uses Edit/Write/Read toolsClaude implements the validated plan
Review codeecho "review changes" | codex exec --sandbox read-onlyCodex validates Claude's implementation
Continue reviewecho "next step" | codex exec resume --lastContinue validation session
Apply fixesClaude uses Edit/Write toolsClaude fixes issues found by Codex
Re-validateecho "verify fixes" | codex exec resume --lastCodex re-checks after fixes

Error Handling

  1. Stop on non-zero exit codes from Codex
  2. Summarize Codex feedback and ask for direction via AskUserQuestion
  3. Before implementing changes, confirm approach with user if:
    • Significant architectural changes needed
    • Multiple files will be affected
    • Breaking changes are required
  4. When Codex warnings appear, Claude evaluates severity and decides next steps

The Perfect Loop

Plan (Claude) → Validate Plan (Codex) → Feedback →
Implement (Claude) → Review Code (Codex) →
Fix Issues (Claude) → Re-validate (Codex) → Repeat until perfect

This creates a self-correcting, high-quality engineering system where:

  • Claude handles all code implementation and modifications
  • Codex provides validation, review, and quality assurance

When not to use it

  • Simple, low-stakes coding tasks
  • Situations where speed is prioritized over validation
  • Non-code logic generation

Prerequisites

Claude Codecodex

Limitations

  • Increased latency due to multiple passes
  • Requires context management between model handoffs

How it compares

It formalizes a multi-model feedback loop that prevents single-model hallucination or logic errors.

Compared to similar skills

codex-claude-loop side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
codex-claude-loop (this skill)139moReviewAdvanced
python-testing-patterns772moReviewIntermediate
error-handling-patterns352moNo flagsIntermediate
serena159moReviewAdvanced

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