Files
Claude-Code-Workflow/.codex/skills/ccw-loop-b/SKILL.md
catlog22 b9f17f0fcf fix: Add required 'name' field to Codex skill YAML frontmatter
According to OpenAI Codex skill specification, SKILL.md files must have both
'name' and 'description' fields in YAML frontmatter. Added missing 'name' field
to all three skills:
- CCW Loop
- CCW Loop-B
- Parallel Dev Cycle

Also enhanced ccw-loop description with Chinese trigger keywords for better
multi-language support.
2026-01-23 12:38:45 +08:00

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---
name: CCW Loop-B
description: Hybrid orchestrator pattern for iterative development. Coordinator + specialized workers with batch wait support. Triggers on "ccw-loop-b".
argument-hint: TASK="<task description>" [--loop-id=<id>] [--mode=<interactive|auto|parallel>]
---
# CCW Loop-B - Hybrid Orchestrator Pattern
协调器 + 专用 worker 的迭代开发工作流。支持单 agent 深度交互、多 agent 并行、混合模式灵活切换。
## Arguments
| Arg | Required | Description |
|-----|----------|-------------|
| TASK | No | Task description (for new loop) |
| --loop-id | No | Existing loop ID to continue |
| --mode | No | `interactive` (default) / `auto` / `parallel` |
## Architecture
```
+------------------------------------------------------------+
| Main Coordinator |
| 职责: 状态管理 + worker 调度 + 结果汇聚 + 用户交互 |
+------------------------------------------------------------+
|
+--------------------+--------------------+
| | |
v v v
+----------------+ +----------------+ +----------------+
| Worker-Develop | | Worker-Debug | | Worker-Validate|
| 专注: 代码实现 | | 专注: 问题诊断 | | 专注: 测试验证 |
+----------------+ +----------------+ +----------------+
```
## Execution Modes
### Mode: Interactive (default)
协调器展示菜单,用户选择 actionspawn 对应 worker 执行。
```
Coordinator -> Show menu -> User selects -> spawn worker -> wait -> Display result -> Loop
```
### Mode: Auto
自动按预设顺序执行worker 完成后自动切换到下一阶段。
```
Init -> Develop -> [if issues] Debug -> Validate -> [if fail] Loop back -> Complete
```
### Mode: Parallel
并行 spawn 多个 worker 分析不同维度batch wait 汇聚结果。
```
Coordinator -> spawn [develop, debug, validate] in parallel -> wait({ ids: all }) -> Merge -> Decide
```
## Session Structure
```
.workflow/.loop/
+-- {loopId}.json # Master state
+-- {loopId}.workers/ # Worker outputs
| +-- develop.output.json
| +-- debug.output.json
| +-- validate.output.json
+-- {loopId}.progress/ # Human-readable progress
+-- develop.md
+-- debug.md
+-- validate.md
+-- summary.md
```
## Subagent API
| API | 作用 |
|-----|------|
| `spawn_agent({ message })` | 创建 agent返回 `agent_id` |
| `wait({ ids, timeout_ms })` | 等待结果(唯一取结果入口) |
| `send_input({ id, message })` | 继续交互 |
| `close_agent({ id })` | 关闭回收 |
## Implementation
### Coordinator Logic
```javascript
// ==================== HYBRID ORCHESTRATOR ====================
// 1. Initialize
const loopId = args['--loop-id'] || generateLoopId()
const mode = args['--mode'] || 'interactive'
let state = readOrCreateState(loopId, taskDescription)
// 2. Mode selection
switch (mode) {
case 'interactive':
await runInteractiveMode(loopId, state)
break
case 'auto':
await runAutoMode(loopId, state)
break
case 'parallel':
await runParallelMode(loopId, state)
break
}
```
### Interactive Mode (单 agent 交互或按需 spawn worker)
```javascript
async function runInteractiveMode(loopId, state) {
while (state.status === 'running') {
// Show menu, get user choice
const action = await showMenuAndGetChoice(state)
if (action === 'exit') break
// Spawn specialized worker for the action
const workerId = spawn_agent({
message: buildWorkerPrompt(action, loopId, state)
})
// Wait for worker completion
const result = wait({ ids: [workerId], timeout_ms: 600000 })
const output = result.status[workerId].completed
// Update state and display result
state = updateState(loopId, action, output)
displayResult(output)
// Cleanup worker
close_agent({ id: workerId })
}
}
```
### Auto Mode (顺序执行 worker 链)
```javascript
async function runAutoMode(loopId, state) {
const actionSequence = ['init', 'develop', 'debug', 'validate', 'complete']
let currentIndex = state.skill_state?.action_index || 0
while (currentIndex < actionSequence.length && state.status === 'running') {
const action = actionSequence[currentIndex]
// Spawn worker
const workerId = spawn_agent({
message: buildWorkerPrompt(action, loopId, state)
})
const result = wait({ ids: [workerId], timeout_ms: 600000 })
const output = result.status[workerId].completed
// Parse worker result to determine next step
const workerResult = parseWorkerResult(output)
// Update state
state = updateState(loopId, action, output)
close_agent({ id: workerId })
// Determine next action
if (workerResult.needs_loop_back) {
// Loop back to develop or debug
currentIndex = actionSequence.indexOf(workerResult.loop_back_to)
} else if (workerResult.status === 'failed') {
// Stop on failure
break
} else {
currentIndex++
}
}
}
```
### Parallel Mode (批量 spawn + wait)
```javascript
async function runParallelMode(loopId, state) {
// Spawn multiple workers in parallel
const workers = {
develop: spawn_agent({ message: buildWorkerPrompt('develop', loopId, state) }),
debug: spawn_agent({ message: buildWorkerPrompt('debug', loopId, state) }),
validate: spawn_agent({ message: buildWorkerPrompt('validate', loopId, state) })
}
// Batch wait for all workers
const results = wait({
ids: Object.values(workers),
timeout_ms: 900000 // 15 minutes for all
})
// Collect outputs
const outputs = {}
for (const [role, workerId] of Object.entries(workers)) {
outputs[role] = results.status[workerId].completed
close_agent({ id: workerId })
}
// Merge and analyze results
const mergedAnalysis = mergeWorkerOutputs(outputs)
// Update state with merged results
updateState(loopId, 'parallel-analysis', mergedAnalysis)
// Coordinator decides next action based on merged results
const decision = decideNextAction(mergedAnalysis)
return decision
}
```
### Worker Prompt Builder
```javascript
function buildWorkerPrompt(action, loopId, state) {
const workerRoles = {
develop: '~/.codex/agents/ccw-loop-b-develop.md',
debug: '~/.codex/agents/ccw-loop-b-debug.md',
validate: '~/.codex/agents/ccw-loop-b-validate.md',
init: '~/.codex/agents/ccw-loop-b-init.md',
complete: '~/.codex/agents/ccw-loop-b-complete.md'
}
return `
## TASK ASSIGNMENT
### MANDATORY FIRST STEPS (Agent Execute)
1. **Read role definition**: ${workerRoles[action]} (MUST read first)
2. Read: .workflow/project-tech.json
3. Read: .workflow/project-guidelines.json
---
## LOOP CONTEXT
- **Loop ID**: ${loopId}
- **Action**: ${action}
- **State File**: .workflow/.loop/${loopId}.json
- **Output File**: .workflow/.loop/${loopId}.workers/${action}.output.json
- **Progress File**: .workflow/.loop/${loopId}.progress/${action}.md
## CURRENT STATE
${JSON.stringify(state, null, 2)}
## TASK DESCRIPTION
${state.description}
## EXPECTED OUTPUT
\`\`\`
WORKER_RESULT:
- action: ${action}
- status: success | failed | needs_input
- summary: <brief summary>
- files_changed: [list]
- next_suggestion: <suggested next action>
- loop_back_to: <action name if needs loop back>
DETAILED_OUTPUT:
<structured output specific to action type>
\`\`\`
Execute the ${action} action now.
`
}
```
## Worker Roles
| Worker | Role File | 专注领域 |
|--------|-----------|----------|
| init | ccw-loop-b-init.md | 会话初始化、任务解析 |
| develop | ccw-loop-b-develop.md | 代码实现、重构 |
| debug | ccw-loop-b-debug.md | 问题诊断、假设验证 |
| validate | ccw-loop-b-validate.md | 测试执行、覆盖率 |
| complete | ccw-loop-b-complete.md | 总结收尾 |
## State Schema
See [phases/state-schema.md](phases/state-schema.md)
## Usage
```bash
# Interactive mode (default)
/ccw-loop-b TASK="Implement user authentication"
# Auto mode
/ccw-loop-b --mode=auto TASK="Fix login bug"
# Parallel analysis mode
/ccw-loop-b --mode=parallel TASK="Analyze and improve payment module"
# Resume existing loop
/ccw-loop-b --loop-id=loop-b-20260122-abc123
```
## Error Handling
| Situation | Action |
|-----------|--------|
| Worker timeout | send_input 请求收敛 |
| Worker failed | Log error, 协调器决策是否重试 |
| Batch wait partial timeout | 使用已完成结果继续 |
| State corrupted | 从 progress 文件重建 |
## Best Practices
1. **协调器保持轻量**: 只做调度和状态管理,具体工作交给 worker
2. **Worker 职责单一**: 每个 worker 专注一个领域
3. **结果标准化**: Worker 输出遵循统一 WORKER_RESULT 格式
4. **灵活模式切换**: 根据任务复杂度选择合适模式
5. **及时清理**: Worker 完成后 close_agent 释放资源