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Task Executor Skill

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uluckyXH
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

Overview

PublisheruluckyXH
RepositoryOpenMOSS
Skill nametask-executor-skill
Stars
1.3K
Forks
146
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by uluckyXH on GitHub. Read the source before you install it.

Installation

Install the Task Executor Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/uluckyXH/OpenMOSS.git /tmp/OpenMOSS
mkdir -p .claude/skills
cp -r /tmp/OpenMOSS/skills/task-executor-skill .claude/skills/task-executor-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task Executor Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Task Executor Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Task Executor Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 startst 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)

Frequently asked questions

What does the Task Executor Skill AI 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

Why use Task Executor Skill on TypingMind?

Because you install it once and use it with any model. Task Executor Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Task Executor Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uluckyXH/OpenMOSS/tree/main/skills/task-executor-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Task Executor Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Task Executor Skill?

As many as you like. As long as a model supports skills, you can use Task Executor Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Task Executor Skill AI skill free?

Yes. It is published on GitHub by uluckyXH under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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