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
npx skills add https://github.com/cafe3310/public-agent-skills --skill doc-todo-log-loop
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。
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。
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:
YYYY-MM-DD-HH-mm-需求-{简述}.md)。tests/ 下准备对应的测试用例集(结构见「测试用例管理」一节)。TODO.md。tests/ 中对应的测试用例。TODO.md 中选择事项并明确指示执行。YYYY-MM-DD-HH-mm-开发日志-{标题}.md),内容包括:TODO.md 并向用户建议下一步任务,但不主动开始。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:
在步骤 1 中直接创建新的时间戳目录,编写测试用例。
-EDITING 后缀。-EDITING 后缀。TODO.md 和开发日志中引用新版本。Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Comprehensive technology-agnostic prompt for analyzing and documenting project folder structures. Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter), generates detailed blueprints with visualization options, naming conventions, file placement patterns, and extension templates for maintaining consistent code organization across diverse technology stacks.
Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
> Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Patterns for automating GitHub workflows with AI assistance, inspired by [Gemini CLI](https://github.com/google-gemini/gemini-cli) and modern DevOps practices.
Groups existing components into logical business domains to plan service-based architecture. Use when asking "which components belong together?", "group these into services", "organize by domain", "component-to-domain mapping", or planning service extraction from an existing codebase. Do NOT use for identifying new domains from scratch (use domain-analysis) or analyzing coupling (use coupling-analysis).
Take cafe3310/doc-todo-log-loop from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.