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optimize-docs优化文档

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

388

周安装

16

GitHub Stars

34

下载量

127
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:optimize-docs(优化文档)
来源仓库:https://github.com/fimoklei/pm-ai-playbook
仓库路径:skills/optimize-docs
安装命令:
npx skills add https://github.com/fimoklei/pm-ai-playbook --skill optimize-docs
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/fimoklei/pm-ai-playbook --skill optimize-docs

简介

用于辅助文档、README 和内容稿件的整理与改写,提升可读性和结构清晰度。

  • 适用于提炼结构、统一术语、补齐章节或检查链接等文档优化场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 使用时应保留项目事实,避免将未确认信息写成确定结论。
  • optimize-docs 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

You optimize markdown documentation files to reduce token count while preserving 100% of semantic meaning and actionable guidance.

Input

User provides:

  • Target file(s) or directory to optimize
  • Optional: specific token reduction target (default: 30-35%)

Optimization Pattern

Apply these transformations systematically:

1. Heading Consolidation

  • Collapse related sections under fewer headings
  • Convert ## X + ## When X → single ## X
  • Merge subsections with similar themes

Before:

## Communication style
...
## When Claude Gives Feedback
...

After:

## Style
...
## Giving Feedback
...

2. Bullet Structure Flattening

  • Convert nested bullets to flat structure with em-dashes (—)
  • Inline explanations instead of sub-bullets
  • Use em-dash for definitions/clarifications

Before:

- **Direct and specific**
  - Give clear, direct feedback and critiques
  - No need for gentle suggestions or hedging
  - Specific examples work better than vague advice

After:

- **Direct and specific** — Clear feedback and critiques. No hedging. Specific examples beat vague advice.

3. Verbal Compression

  • Remove filler phrases: "in any context", "you should", "it is important to"
  • Convert full sentences to fragments
  • Use imperative mood consistently
  • Eliminate redundant explanations

Examples:

  • "Give clear, direct feedback" → "Clear feedback"
  • "You should always validate" → "Validate"
  • "It is important to use" → "Use"
  • "Don't take suggestions for granted; challenge them and propose better alternatives" → "Challenge suggestions and propose better alternatives when appropriate"

4. Inline Examples

  • Move examples from sub-bullets into parentheses
  • Keep examples concise and illustrative

Before:

- Specific examples work better than vague advice
  - Example: "Cut the Kizik story" vs "make it shorter"

After:

- Specific examples beat vague advice ("Cut the Kizik story" vs "make it shorter").

5. List Condensation

  • Merge similar list items
  • Remove obvious implications
  • Combine related concepts

6. Preserve Critical Elements

NEVER remove or simplify:

  • Technical accuracy
  • Actionable guidance
  • Specific examples that clarify meaning
  • Checklists
  • Code blocks
  • Semantic distinctions

Workflow

  1. Analyze — Read target file(s), count current tokens/lines
  2. Plan — Identify sections for each optimization pattern
  3. Show preview — Display 2-3 example transformations for user approval
  4. Execute — Apply optimizations across entire file
  5. Verify — Show before/after token counts and reduction percentage
  6. Confirm — Ensure no semantic meaning lost

Output Format

After optimization, provide:

Optimized: [filename]
Before: [X] lines, ~[Y] tokens
After: [X] lines, ~[Y] tokens
Reduction: [Z]%

Key changes:
- [summary of major transformations]

Multi-File Mode

When given a directory:

  1. List all markdown files with current sizes
  2. Process files one at a time
  3. Checkpoint after each file completion
  4. Provide running total of tokens saved
  5. Allow user to review/confirm before moving to next file

Quality Checks

Before marking complete:

  • All semantic meaning preserved
  • Technical accuracy maintained
  • Examples still clear and illustrative
  • No broken markdown syntax
  • Headings still scannable
  • Checklists intact
  • Token reduction achieved (25%+ minimum)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.15%
按下载量换算46

Claude

32.76%
按下载量换算42

Cursor

17.43%
按下载量换算22

Gemini CLI

9.31%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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