Skill 詳細

codex-claude-cursor-loop

AI-assisted engineering workflow.

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出典bear2u/my-skills外部ソース
報告インストール数40人気度の参考値

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SKILL.md

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---
name: codex-claude-cursor-loop
description: Orchestrates a triple-AI engineering loop where Claude plans, Codex validates logic and reviews code, and Cursor implements, with continuous feedback for optimal code quality
---

# Codex-Claude-Cursor Engineering Loop Skill

## Core Workflow Philosophy
This skill implements a 3-way sequential validation engineering loop:
- **Claude Code**: Architecture and planning, final review
- **Codex**: Plan validation (logic/security), code review (bugs/performance)
- **Cursor Agent**: Code implementation and execution
- **Sequential Validation**: Claude plans → Codex validates → Cursor implements → Codex reviews → Claude final check → repeat

## 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:
```bash
   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 <model> --config model_reasoning_effort="<effort>" --sandbox read-only
```
3. Capture Codex's feedback and summarize to user

## Phase 3: Plan Refinement Loop
If Codex finds issues in the plan:
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 implementation?"
4. Repeat Phase 2 if needed until plan is solid

## Phase 4: Implementation with Cursor Agent
Once the plan is validated by Codex:

### Session Management
1. Ask user (via `AskUserQuestion`): "Do you want to start a new Cursor session or resume an existing one?"
   - **New session**: Start fresh
   - **Resume session**: Continue previous work

2. If resuming:
```bash
   # List available sessions
   cursor-agent ls

   # Let user select session ID
   # Store session ID for subsequent calls
```

3. Ask user (via `AskUserQuestion`): Which Cursor model to use (e.g., `composer-1`, `claude-3.5-sonnet`, `gpt-4o`)

### Implementation
4. Send the validated plan to Cursor Agent:

**For new session:**
```bash
   cursor-agent --model "<model-name>" -p --force "Implement this plan:
   [Validated plan here]

   Please implement the code following these specifications exactly."
```

**For resumed session:**
```bash
   cursor-agent --resume="<session-id>" -p --force "Continue implementation:
   [Validated plan here]"
```

5. **IMPORTANT**: Store the session ID from the output for all subsequent Cursor calls
6. Capture what was implemented and which files were modified

## Phase 5: Codex Code Review
After Cursor implements:
1. Send Cursor's implementation to Codex for code review:
```bash
   echo "Review this implementation for:
   - Bugs and logic errors
   - Performance issues
   - Security vulnerabilities
   - Best practices violations
   - Code quality concerns

   Files modified: [list of files]
   Implementation summary: [what Cursor did]" | codex exec --sandbox read-only
```
2. Capture Codex's code review feedback
3. Summarize findings to user

## Phase 6: Claude's Final Review
After Codex code review:
1. Claude reads the implemented code using Read tool
2. Claude analyzes both:
   - Codex's review findings
   - The actual implementation
3. Claude provides final assessment:
   - Verify if it matches the original plan
   - Confirm Codex's findings are valid
   - Identify any additional concerns
   - Make final architectural decisions
4. Summarize overall quality and readiness

## Phase 7: Iterative Improvement Loop
If issues are found (by Codex or Claude):
1. Claude creates a detailed fix plan based on:
   - Codex's code review findings
   - Cla
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