home / skills / catlog22 / claude-code-workflow / codex-issue-plan-execute
/.codex/skills/codex-issue-plan-execute
This skill orchestrates autonomous issue planning and execution for Codex, batching tasks, maintaining persistent dual agents, and generating unified planning
npx playbooks add skill catlog22/claude-code-workflow --skill codex-issue-plan-executeReview the files below or copy the command above to add this skill to your agents.
---
name: codex-issue-plan-execute
description: Autonomous issue planning and execution workflow for Codex. Supports batch issue processing with integrated planning, queuing, and execution stages. Triggers on "codex-issue", "plan execute issue", "issue workflow".
allowed-tools: Task, AskUserQuestion, Read, Write, Bash, Glob, Grep
---
# Codex Issue Plan-Execute Workflow
Streamlined autonomous workflow for Codex that integrates issue planning, queue management, and solution execution in a single stateful Skill. Supports batch processing with minimal queue overhead and dual-agent execution strategy.
## Architecture Overview
For complete architecture details, system diagrams, and design principles, see **[ARCHITECTURE.md](ARCHITECTURE.md)**.
Key concepts:
- **Persistent Dual-Agent System**: Two long-running agents (Planning + Execution) that maintain context across all tasks
- **Sequential Pipeline**: Issues → Planning Agent → Solutions → Execution Agent → Results
- **Unified Results**: All results accumulated in single `planning-results.json` and `execution-results.json` files
- **Efficient Communication**: Uses `send_input()` for task routing without agent recreation overhead
---
## ⚠️ Mandatory Prerequisites (强制前置条件)
> **⛔ 禁止跳过**: 在执行任何操作之前,**必须**阅读以下两份P0规范文档。未理解规范直接执行将导致输出质量不符合标准。
| Document | Purpose | When |
|----------|---------|------|
| [specs/issue-handling.md](specs/issue-handling.md) | Issue 处理规范和数据结构 | **执行前必读** |
| [specs/solution-schema.md](specs/solution-schema.md) | 解决方案数据结构和验证规则 | **执行前必读** |
---
## Execution Flow
### Phase 1: Initialize Persistent Agents
→ **查阅**: [ARCHITECTURE.md](ARCHITECTURE.md) - 系统架构
→ **查阅**: [phases/orchestrator.md](phases/orchestrator.md) - 编排逻辑
→ Spawn Planning Agent with `prompts/planning-agent.md` (stays alive)
→ Spawn Execution Agent with `prompts/execution-agent.md` (stays alive)
### Phase 2: Planning Pipeline
→ **查阅**: [phases/actions/action-plan.md](phases/actions/action-plan.md), [specs/subagent-roles.md](specs/subagent-roles.md)
For each issue sequentially:
1. Send issue to Planning Agent via `send_input()` with planning request
2. Wait for Planning Agent to return solution JSON
3. Store result in unified `planning-results.json` array
4. Continue to next issue (agent stays alive)
### Phase 3: Execution Pipeline
→ **查阅**: [phases/actions/action-execute.md](phases/actions/action-execute.md), [specs/quality-standards.md](specs/quality-standards.md)
For each successful planning result sequentially:
1. Send solution to Execution Agent via `send_input()` with execution request
2. Wait for Execution Agent to complete implementation and testing
3. Store result in unified `execution-results.json` array
4. Continue to next solution (agent stays alive)
### Phase 4: Finalize
→ **查阅**: [phases/actions/action-complete.md](phases/actions/action-complete.md)
→ Close Planning Agent (after all issues planned)
→ Close Execution Agent (after all solutions executed)
→ Generate final report with statistics
### State Schema
```json
{
"status": "pending|running|completed",
"phase": "init|listing|planning|executing|complete",
"issues": {
"{issue_id}": {
"id": "ISS-xxx",
"status": "registered|planning|planned|executing|completed",
"solution_id": "SOL-xxx-1",
"planned_at": "ISO-8601",
"executed_at": "ISO-8601"
}
},
"queue": [
{
"item_id": "S-1",
"issue_id": "ISS-xxx",
"solution_id": "SOL-xxx-1",
"status": "pending|executing|completed"
}
],
"context": {
"work_dir": ".workflow/.scratchpad/...",
"total_issues": 0,
"completed_count": 0,
"failed_count": 0
},
"errors": []
}
```
---
## Directory Setup
```javascript
const timestamp = new Date().toISOString().slice(0,19).replace(/[-:T]/g, '');
const workDir = `.workflow/.scratchpad/codex-issue-${timestamp}`;
Bash(`mkdir -p "${workDir}"`);
Bash(`mkdir -p "${workDir}/solutions"`);
Bash(`mkdir -p "${workDir}/snapshots"`);
```
## Output Structure
```
.workflow/.scratchpad/codex-issue-{timestamp}/
├── planning-results.json # All planning results in single file
│ ├── phase: "planning"
│ ├── created_at: "ISO-8601"
│ └── results: [
│ { issue_id, solution_id, status, solution, planned_at }
│ ]
├── execution-results.json # All execution results in single file
│ ├── phase: "execution"
│ ├── created_at: "ISO-8601"
│ └── results: [
│ { issue_id, solution_id, status, commit_hash, files_modified, executed_at }
│ ]
└── final-report.md # Summary statistics and report
```
---
## Reference Documents by Phase
### 🔧 Setup & Understanding (初始化阶段)
用于理解整个系统架构和执行流程
| Document | Purpose | Key Topics |
|----------|---------|-----------|
| [phases/orchestrator.md](phases/orchestrator.md) | 编排器核心逻辑 | 如何管理agents、pipeline流程、状态转换 |
| [phases/state-schema.md](phases/state-schema.md) | 状态结构定义 | 完整状态模型、验证规则、持久化 |
| [specs/agent-roles.md](specs/agent-roles.md) | Agent角色和职责定义 | Planning & Execution Agent详细说明 |
### 📋 Planning Phase (规划阶段)
执行Phase 2时查阅 - Planning逻辑和Issue处理
| Document | Purpose | When to Use |
|----------|---------|-------------|
| [phases/actions/action-plan.md](phases/actions/action-plan.md) | Planning流程详解 | 理解issue→solution转换逻辑 |
| [phases/actions/action-list.md](phases/actions/action-list.md) | Issue列表处理 | 学习issue加载和列举逻辑 |
| [specs/issue-handling.md](specs/issue-handling.md) | Issue数据规范 | 理解issue结构和验证规则 ✅ **必读** |
| [specs/solution-schema.md](specs/solution-schema.md) | 解决方案数据结构 | 了解solution JSON格式 ✅ **必读** |
### ⚙️ Execution Phase (执行阶段)
执行Phase 3时查阅 - 实现和验证逻辑
| Document | Purpose | When to Use |
|----------|---------|-------------|
| [phases/actions/action-execute.md](phases/actions/action-execute.md) | Execution流程详解 | 理解solution→implementation逻辑 |
| [specs/quality-standards.md](specs/quality-standards.md) | 质量标准和验收条件 | 检查implementation是否达标 |
### 🏁 Completion Phase (完成阶段)
执行Phase 4时查阅 - 收尾和报告逻辑
| Document | Purpose | When to Use |
|----------|---------|-------------|
| [phases/actions/action-complete.md](phases/actions/action-complete.md) | 完成流程 | 生成最终报告、统计信息 |
### 🔍 Debugging & Troubleshooting (问题排查)
遇到问题时查阅 - 快速定位和解决
| Issue | Solution Document |
|-------|------------------|
| 执行过程中状态异常 | [phases/state-schema.md](phases/state-schema.md) - 验证状态结构 |
| Planning Agent输出不符合预期 | [phases/actions/action-plan.md](phases/actions/action-plan.md) + [specs/solution-schema.md](specs/solution-schema.md) |
| Execution Agent实现失败 | [phases/actions/action-execute.md](phases/actions/action-execute.md) + [specs/quality-standards.md](specs/quality-standards.md) |
| Issue数据格式错误 | [specs/issue-handling.md](specs/issue-handling.md) |
### 📚 Architecture & Agent Definitions (架构和Agent定义)
核心设计文档
| Document | Purpose | Notes |
|----------|---------|-------|
| [ARCHITECTURE.md](ARCHITECTURE.md) | 系统架构和设计原则 | 启动前必读 |
| [specs/agent-roles.md](specs/agent-roles.md) | Agent角色定义 | Planning & Execution Agent详细职责 |
| [prompts/planning-agent.md](prompts/planning-agent.md) | Planning Agent统一提示词 | 用于初始化Planning Agent |
| [prompts/execution-agent.md](prompts/execution-agent.md) | Execution Agent统一提示词 | 用于初始化Execution Agent |
---
## Usage Examples
### Batch Process Specific Issues
```bash
codex -p "@.codex/prompts/codex-issue-plan-execute ISS-001,ISS-002,ISS-003"
```
### Interactive Selection
```bash
codex -p "@.codex/prompts/codex-issue-plan-execute"
# Then select issues from the list
```
### Resume from Snapshot
```bash
codex -p "@.codex/prompts/codex-issue-plan-execute --resume snapshot-path"
```
---
*Skill Version: 1.0*
*Execution Mode: Autonomous*
*Status: Ready for Customization*
This skill provides an autonomous issue planning and execution workflow for Codex, combining planning, queuing, and execution in a single stateful tool. It supports batch issue processing and maintains persistent planning and execution agents to reduce overhead. The skill outputs unified planning and execution result files and a final report for auditability.
The workflow spawns two persistent agents: a Planning Agent that converts issues into validated solution JSON, and an Execution Agent that implements and tests those solutions. Issues are processed sequentially: planning results are appended to planning-results.json, then successful solutions are queued for execution and recorded in execution-results.json. State is tracked in a JSON schema to allow resuming, reporting, and minimal agent recreation through send_input() routing.
Do I have to read any documents before running this workflow?
Yes. You must review the issue handling and solution schema specifications to ensure inputs and outputs conform to required formats.
Can the workflow be resumed after interruption?
Yes. The state schema and snapshot/resume mechanism allow you to pick up from where the pipeline left off and continue planning or execution.