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Proj Optimize

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zhangloveyan
proj-optimize

持续优化与自我进化。收集开发过程中的问题和优化点，批量处理积累的优化需求，验证优化效果。

Overview

Publisherzhangloveyan
Repositorybackend-skill
Skill nameproj-optimize
Stars
102
Forks
31
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Proj Optimize 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/zhangloveyan/backend-skill/tree/main/.claude/skills/proj-optimize
  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/zhangloveyan/backend-skill.git /tmp/backend-skill
mkdir -p .claude/skills
cp -r /tmp/backend-skill/.claude/skills/proj-optimize .claude/skills/proj-optimize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proj Optimize 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 Proj Optimize 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 Proj Optimize 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.

持续优化与自我进化

适用场景

  • 开发过程中发现规范缺失或流程问题
  • 项目结束后批量优化积累的问题
  • 定期检查和改进开发规范体系

任务文档同步

  • 如存在全流程任务文档，将优化相关文件记录到“扩展产物”
  • 上下文快照记录优化状态（不写细节）
  • 更新下一步指令

使用方式

/proj-optimize record    # 记录问题和优化点
/proj-optimize          # 执行批量优化（默认模式）
/proj-optimize check    # 检查是否有待优化项
/proj-optimize clean    # 清理已完成的优化记录

1. 记录模式 (/proj-optimize record)

存储位置

问题记录保存在项目本地文件中：

  • 待优化问题：docs/optimization/pending-issues.md
  • 优化历史：docs/optimization/optimization-log.md

优势：

  • 项目级隔离：不同项目的优化问题分开管理
  • 持久保存：本地文件，不会丢失
  • 版本控制：可纳入 Git 管理，团队共享
  • 避免臃肿：每个项目只关注自己的优化点

使用时机

  • 开发中遇到规范缺失
  • 发现流程执行问题
  • Skill 使用不便
  • 重复性问题出现

记录内容

每次记录包含以下信息：

  • 问题描述：具体遇到什么问题
  • 影响范围：对开发的影响
  • 优化建议：初步的解决思路
  • 优先级：P0(阻塞) / P1(重要) / P2(优化)
  • 涉及文件：相关的 Skill 或文档文件

记录方式

  1. 打开 docs/optimization/pending-issues.md
  2. 根据优先级添加到对应分类下
  3. 使用模板格式记录问题详情
  4. 保存文件
  5. 同步任务文档（扩展产物与上下文快照）

记录模板

参考 问题记录模板

2. 执行模式 (/proj-optimize)

执行流程

  1. 读取问题列表：从 docs/optimization/pending-issues.md 读取待优化问题
  2. 优先级排序：P0 → P1 → P2 顺序处理
  3. 生成优化方案：分析每个问题，生成详细的修改方案
  4. 用户确认：逐项展示优化方案，等待用户确认
  5. 执行修改：只执行用户确认的优化项
  6. 验证改进：确认优化是否解决问题
  7. 更新记录：将完成的问题移动到 docs/optimization/optimization-log.md
  8. 同步文档：必要时更新 CLAUDE.md 索引
  9. 同步任务文档：更新扩展产物与下一步指令

用户确认机制

每个优化方案都会详细展示：

  • 修改文件：具体要修改哪些文件
  • 修改内容：详细的修改说明
  • 影响范围：对现有功能的影响
  • 风险评估：修改的风险等级

确认选项：

  • y - 执行此优化
  • n - 跳过此优化
  • s - 跳过所有剩余优化
  • a - 执行所有剩余优化（批量确认）

优化方案预览格式

markdown
## 优化方案预览

### 问题 1/3: [P1] 代码生成缺少异常处理模板

**修改文件**：
- `.claude/skills/proj-gen/templates/service.md`

**修改内容**：
- 在 Service 模板的方法中增加 try-catch 异常处理
- 添加 BusinessException 转换逻辑

**影响范围**：
- 后续使用 /proj-gen 生成的 Service 代码将包含异常处理
- 不影响已有代码

**风险评估**：低风险，向后兼容

**是否执行此优化？**
[y] 是  [n] 否  [s] 跳过所有  [a] 全部执行

优化原则

  • 基于实践：所有优化都来自真实开发经验
  • 用户主导：所有修改都需要用户明确确认
  • 小步快跑：每次优化1-3个问题，避免大幅变动
  • 向后兼容：尽量保持现有使用方式不变
  • 验证效果：优化后在下次开发中验证是否有效

安全保障

  • 预览优先：先展示修改方案，不立即执行
  • 逐项确认：每个修改都需要用户明确同意
  • 批量选项：提供批量确认，提高效率
  • 记录选择：记录用户的确认/拒绝选择，便于后续参考

智能跳过

  • 如果 docs/optimization/pending-issues.md 中没有待处理问题
  • 自动提示"当前无待优化项，跳过执行"
  • 避免无意义的重复操作

3. 检查模式 (/proj-optimize check)

功能

  • 读取 docs/optimization/pending-issues.md 文件
  • 统计各优先级问题数量
  • 给出优化建议

输出示例

优化状态检查：
- 待优化问题：5个
  - P0 (阻塞)：1个
  - P1 (重要)：2个
  - P2 (优化)：2个
- 建议：存在P0问题，建议立即执行优化

检查文件

  • docs/optimization/pending-issues.md - 待优化问题
  • docs/optimization/optimization-log.md - 历史记录（可选）

4. 清理模式 (/proj-optimize clean)

功能

  • 清理 docs/optimization/pending-issues.md 中已标记为完成的问题
  • 整理 docs/optimization/optimization-log.md 历史记录
  • 保持文件整洁，便于查看

清理规则

  • 移除状态为"已完成"的问题记录
  • 保留模板和说明部分
  • 整理历史记录的格式

优化触发点

触发场景记录方式优先级
开发阻塞立即记录，标记P0立即优化
流程不顺开发结束后记录，标记P1项目结束后优化
使用不便积累记录，标记P2定期批量优化
重复问题第二次出现时记录，标记P1优先处理

自我进化机制

进化路径

开发实践 → 发现问题 → /proj-optimize record → 积累问题 → /proj-optimize → 验证效果 → 固化经验

持续改进循环

  1. 实践中发现：每次开发都可能发现新问题
  2. 及时记录：使用 record 模式立即记录
  3. 批量优化：积累一定数量后统一处理
  4. 验证效果：下次开发中验证改进是否有效
  5. 持续迭代：无效的优化继续改进，有效的固化为规范

质量保证

  • 问题追踪：每个问题都有完整的生命周期记录
  • 效果验证：优化后必须在实际开发中验证
  • 回滚机制：无效优化可以回滚到之前版本
  • 知识积累：成功的优化经验固化为最佳实践

最佳实践

记录问题时

  • 描述要具体，避免模糊表达
  • 提供初步解决思路，便于后续优化
  • 标记准确的优先级，合理安排优化顺序

执行优化时

  • 一次处理1-3个问题，避免过度变动
  • 优化后立即在小范围验证效果
  • 保留优化前的版本，便于必要时回滚

验证效果时

  • 在真实开发场景中测试改进效果
  • 收集使用反馈，持续调整优化方案
  • 将验证结果记录，为后续优化提供参考

通过这套机制，整个开发规范体系能够基于实践不断自我完善，最终实现真正的自我进化。

模板文件

模板文件说明
问题记录模板templates/issue-template.md记录问题的标准格式和示例
优化记录模板templates/optimization-log-template.md记录优化历史的标准格式

项目文件结构

docs/
└── optimization/
    ├── pending-issues.md           # 待优化问题列表
    └── optimization-log.md         # 优化历史记录

优势：

  • 项目隔离：每个项目独立管理优化问题
  • 持久保存：本地文件，不会丢失
  • 版本控制：可纳入 Git，团队共享
  • 避免臃肿：不同项目的问题分开，保持精简

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Proj Optimize AI skill do?

持续优化与自我进化。收集开发过程中的问题和优化点，批量处理积累的优化需求，验证优化效果。

Why use Proj Optimize on TypingMind?

Because you install it once and use it with any model. Proj Optimize 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 Proj Optimize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhangloveyan/backend-skill/tree/main/.claude/skills/proj-optimize. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Proj Optimize?

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 Proj Optimize?

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

Is the Proj Optimize AI skill free?

Yes. It is published on GitHub by zhangloveyan 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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