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Doc Todo Log Loop

Community
cafe3310
doc-todo-log-loop

基于日志记录驱动的轻量级项目开发和管理方案,是新项目的缺省管理方案

Overview

Publishercafe3310
Repositorypublic-agent-skills
Skill namedoc-todo-log-loop
Stars
256
Forks
30
Bundled files
Instructions only
LicenseApache-2.0
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 cafe3310 on GitHub. Read the source before you install it.

Installation

Install the Doc Todo Log Loop 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.
    https://github.com/cafe3310/public-agent-skills/tree/main/skills/doc-todo-log-loop
  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/cafe3310/public-agent-skills.git /tmp/public-agent-skills
mkdir -p .claude/skills
cp -r /tmp/public-agent-skills/skills/doc-todo-log-loop .claude/skills/doc-todo-log-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doc Todo Log Loop 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 Doc Todo Log Loop 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 Doc Todo Log Loop 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.

Skill: doc-todo-log-loop

1. 概述

文档驱动、日志记录的人机协作开发工作流。用户控制开发节奏,Agent 负责文档撰写、任务拆分、开发执行和日志记录。

2. 概念定义

以下为默认路径和命名约定。项目章程有特别约定时,以项目章程为准。

  • 文档目录: 默认为项目根目录下 backlog/,存放需求、设计、日志等文档
  • TODO 文件: 默认项目根目录下 TODO.md
  • 测试目录: 默认项目根目录下 tests/
  • 文档命名: YYYY-MM-DD-HH-mm-{类别}-{标题}.md
  • 测试用例集命名: tests/{YYYY-MM-DD-HH-mm}-testsuite/
  • 测试用例命名: part{序号}-{模块}/case{序号}-{简述}.md,在测试用例集内部

3. 文档类别和编写风格

除了后面的工作流之外,若用户要求,Agent 可以随时写文档。 文档命名中的 {类别} 按以下分类取值:

  • 开发日志: 开发过程、决策、问题及解决方式的记录。
  • 需求: 用户想要实现的功能或目标。仅含需求本身,不含实现细节。
  • 设计: 对即将实施任务的提前分析。子类别:系统设计、架构设计、交互设计、需求设计。
  • 规范: 定义广泛适用的规则、流程或标准。子类别:架构规范、代码规范、流程规范。
  • 说明: 对已完成技术实体的使用说明。子类别:接口说明、模块说明。
  • 调研: 对外部技术或资料的研究与对比分析。广泛搜索,记录来源,写出细节而非过度摘要。
  • 参考: 从外部摘录的原始资料。与调研的区别在于侧重原样引用而非主动分析。

文档的主要读者是未来的 Agent 和开发者。所有文档、过程日志与说明均保持精简、克制、平和、去形容词、去比喻化。无需冗长描述。记录人类决策、问题和修正方案。提供检索和理解所需的最小说明即可。

当用户指示或流程需要进行互联网调研时:广泛搜索相关资料;每找到一份资料,即记录为一份独立的调研文档,按命名约定命名;文档要记录来源,写出方案、观点、方法的细节,不要过度摘要。

4. 主要工作循环

本 Skill 定义的主要工作流由用户和 Agent 交替执行,遵循以下步骤:

步骤 1: 背景描述 → 文档撰写

  • 触发: 用户提出功能目标或问题背景。
  • Agent 行动:
    1. 与用户沟通,理解背景、目标、约束。
    2. 撰写需求描述文档(命名:YYYY-MM-DD-HH-mm-需求-{简述}.md)。
    3. 如有 Plan Mode 中已接受的 Plan 文件,移动到文档目录并合理命名。
    4. 如项目含测试,在 tests/ 下准备对应的测试用例集(结构见「测试用例管理」一节)。

步骤 2: 需求描述 → TODO 拆分

  • 触发: 用户基于文档或直接提出具体需求。
  • Agent 行动:
    1. 将需求拆解为具体、原子化的待办事项,更新到 TODO.md。
    2. 每个事项关联 tests/ 中对应的测试用例。

步骤 3: 任务指派

  • 触发: 用户从 TODO.md 中选择事项并明确指示执行。
  • Agent 行动: 确认指令,了解必要文档,了解测试诉求,然后进入开发和验证。

步骤 4: 开发与验证

  • 触发: 用户下达开发指令。
  • Agent 行动:
    1. 执行开发任务。
    2. 每完成一个功能点,按关联的测试用例逐项验证(测试用例为人工检查清单,Agent 按步骤操作并记录结果)。
    3. 向用户报告时必须包含验证结果(通过/失败/待观察)。
    4. 用户进行最终确认。
  • 约束: 验证未通过时,禁止声称任务完成。

步骤 5: 开发日志记录

  • 触发: TODO 事项经用户确认完成后。
  • Agent 行动:
    1. 撰写开发日志(命名:YYYY-MM-DD-HH-mm-开发日志-{标题}.md),内容包括:
      • 实现了哪些功能点
      • 对代码或项目结构的主要修改
      • 遇到的问题及修正方式(包括用户和 Agent)
      • 后续步骤建议
    2. 如经过测试,日志中包含验证结果:执行了哪些用例、对应的用例集版本、验证结论。
    3. 日志完成后,可查阅 TODO.md 并向用户建议下一步任务,但不主动开始。

步骤 6: 版本控制

  • 触发: 开发日志撰写完成。
  • 用户行动: 审查变更,并执行 git 提交。Agent 不负责此步骤。

5. 测试用例管理

测试用例为 Markdown 格式的人工检查清单,存放在 tests/ 目录下,以时间戳目录进行版本化:

tests/YYYY-MM-DD-HH-mm-testsuite/
├── readme.md                         # 概述、环境要求、执行方法
├── part1-{模块名}/
│   ├── case1-{简述}.md
│   └── case2-{简述}.md
└── part2-{模块名}/

每个测试用例文档包含:

  • ID & 标题: 唯一标识
  • 前置条件: 执行测试所需的初始状态
  • 输入/操作: 具体的执行步骤或数据
  • 预期结果: 明确的成功指标
  • 实际结果: (可选)本次运行的观察,也可在开发日志中记录

首次创建

在步骤 1 中直接创建新的时间戳目录,编写测试用例。

版本更新

  1. 将当前最新的测试用例集目录完整复制到新的时间戳目录,加 -EDITING 后缀。
  2. 在新目录下增删改,直到用户确认。
  3. 去掉 -EDITING 后缀。
  4. 在 TODO.md 和开发日志中引用新版本。

Frequently asked questions

What does the Doc Todo Log Loop AI skill do?

基于日志记录驱动的轻量级项目开发和管理方案,是新项目的缺省管理方案

Why use Doc Todo Log Loop on TypingMind?

Because you install it once and use it with any model. Doc Todo Log Loop 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 Doc Todo Log Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cafe3310/public-agent-skills/tree/main/skills/doc-todo-log-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Doc Todo Log Loop?

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 Doc Todo Log Loop?

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

Is the Doc Todo Log Loop AI skill free?

Yes. It is published on GitHub by cafe3310 under the Apache-2.0 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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