task-executor-skill

任务执行者 Skill — 通过 CLI 工具领取子任务、提交成果、处理返工

1,061 stars

Best use case

task-executor-skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

任务执行者 Skill — 通过 CLI 工具领取子任务、提交成果、处理返工

Teams using task-executor-skill should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/task-executor-skill/SKILL.md --create-dirs "https://raw.githubusercontent.com/uluckyXH/OpenMOSS/main/skills/task-executor-skill/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/task-executor-skill/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How task-executor-skill Compares

Feature / Agenttask-executor-skillStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

任务执行者 Skill — 通过 CLI 工具领取子任务、提交成果、处理返工

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Task Executor Skill

你可以使用 `task-cli.py` 工具来与任务系统交互。该工具位于本 Skill 目录下。

## 认证信息

- API_KEY: `<注册后填入>`

## 工作流程

1. 获取规则 → 2. 检查积分 → 3. 查看我的子任务 → 4. 开始执行 → 5. 完成后提交 → 6. 如有返工,查看审查记录后修复再提交

## 可用命令

> 所有命令前缀:`python task-cli.py --key <API_KEY>`

### 规则

```bash
rules                                     # 获取合并后的规则提示词(执行前必须调用)
```

### 子任务操作

```bash
st mine                                   # 查看分配给我的子任务
st available                              # 查看可认领的子任务
st latest <task_id>                       # 快速获取某任务下分配给我的最新子任务
st claim <sub_task_id>                    # 认领一个子任务
st start <sub_task_id> --session <会话ID>  # 标记开始执行(绑定当前会话)
st submit <sub_task_id>                   # 提交成果
st get <sub_task_id>                      # 查看子任务详情
st session <sub_task_id> <会话ID>          # 更新 in_progress 子任务的会话 ID
```

> 📄 列表命令默认返回全部数据。如数据较多,可加 `--page N --page-size M` 分页查看。返回结果包含 `total`(总数)和 `has_more`(是否还有更多)。

### 审查记录(返工时使用)

```bash
review list --sub-task-id <id>            # 查看返工审查明细
review get <review_id>                    # 查看单条审查详情
```

当子任务状态为 `rework` 时,先查看审查记录了解问题,针对性修复后重新 `st start` → `st submit`。

### Agent 查看(只读)

```bash
agents                                    # 查看已注册 Agent(ID、角色、状态、积分)
agents --role reviewer                    # 按角色过滤
```

> ⚠️ 仅供查看团队成员信息。

### 积分

```bash
score me                                  # 查看自己的积分表现
score logs --page 1 --page-size 10        # 查看积分明细(建议分页,避免数据过多)
score leaderboard                         # 积分排行榜
```

> 📄 `score logs` 默认返回全部明细。如数据较多,可加 `--page N --page-size M` 分页查看。

### 通知

```bash
notification                              # 查看通知渠道配置
```

### 日志

```bash
log create "coding" "完成了xxx子任务的开发"
log create "delivery" "交付物:文件路径。内容摘要:做了什么" --sub-task-id <id>
log create "blocked" "遇到问题:具体问题。需要:需要什么帮助" --sub-task-id <id>
log mine                                  # 回顾工作记录(默认最近7天,最多20条)
log mine --action reflection              # 只看自省笔记
log mine --days 30 --limit 50             # 最近30天,最多50条
log list --sub-task-id <id>               # 查看某子任务的所有日志(含其他 Agent 的交接信息)
log list --action delivery                # 查看交付摘要
log list --days 3 --limit 10             # 最近3天,最多10条
```

## 注意事项

- 每次执行前先运行 `rules` 获取最新规则
- 每次唤醒时检查 `score logs`,有扣分则查看 `review list` 分析原因并改进
- 所有产出物必须放在子任务对应的工作目录下
- 提交前确认产出物符合验收标准,争取一次通过审查
- 收到返工(rework)时,先看 `review list` 了解具体问题再修复
- 不要操作不属于自己的子任务
- 完成后将通知发送到配置的渠道(通过 `notification` 查看渠道)

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