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Agent Skill · 前端设计

doc-condenser(文档冷凝器)

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。

doc-condenser 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 4,965。

总安装 766周安装 31GitHub Stars 216下载 241核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:doc-condenser(文档冷凝器)
来源仓库:https://github.com/mathews-tom/armory
仓库路径:skills/doc-condenser
安装命令:
npx skills add https://github.com/mathews-tom/armory --skill doc-condenser
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/mathews-tom/armory --skill doc-condenser

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

将冗长技术文档压缩为简洁开发者友好规格。适用于路径优先、表格化与示例保留原则转换。已弃用,适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。现代模型原生具备可比质量总结能力。

doc-condenser 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/doc-condenser

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mathews-tom/armory --skill doc-condenser;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

doc-condenser 怎么安装?

在终端执行 npx skills add https://github.com/mathews-tom/armory --skill doc-condenser。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

doc-condenser 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

doc-condenser 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

doc-condenser 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

doc-condenser 用的人多吗?

公开数据是累计安装 766 次,近一周安装 31 次,下载 241 次,GitHub 216 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

doc-condenser 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
doc-condenser

文档冷凝器 · 本页

766216需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

DEPRECATED — Modern Claude models condense and summarize technical documentation natively with comparable quality. The output format preferences encoded here (40% length cap, tables over prose, paths first) are too generic to justify skill overhead. Retained for archival reference only.

Document Condenser

Transform verbose technical documentation into concise, developer-focused specs.

Core Principles

  1. Paths first - Every file reference includes full/relative path
  2. Tables over prose - Use tables for metrics, coverage, file lists
  3. Code samples stay - Keep small, illustrative snippets; remove verbose examples
  4. Commentary, not explanation - Brief context sentences, not paragraphs
  5. One-line history - Reference legacy docs, don't preserve their content

Output Structure

# [path/to/output.md]

# [Title] - [Subtitle if needed]

## Purpose

[2-3 sentences: what this is, why it exists, key design principle]

## Status

[Table: metrics, rates, performance]

## Architecture Overview

[Optional diagram or brief flow description]
[Only if it aids understanding]

## Implementation Files

[Grouped by category with paths and one-line descriptions]

## [Domain-Specific Sections]

[Tables, code snippets, brief commentary as needed]

## Quick Reference

[Box or code block with key stats for scanning]

See assets/template.md for a copy-ready scaffold of this structure.

Transformation Rules

KEEP

  • File paths (always full or project-relative)
  • Metrics and measurements
  • Code snippets under 15 lines that illustrate patterns
  • Schema examples and data structures
  • Coverage/status tables

CONDENSE

  • Multi-paragraph explanations → 1-2 sentences
  • Verbose examples → representative snippet + "see X for more"
  • Implementation checklists → completion status table
  • Long rationales → single "Design principle: X" line
  • Code snippets longer than 20 lines → condense to the core pattern + a reference comment pointing to the source file
  • Rationale sections where the same point is restated across more than 3 sentences → collapse to one "Design principle:" line

REMOVE

  • Historical context beyond one reference line
  • Achieved/completed celebration language
  • Redundant explanations of the same concept
  • Step-by-step tutorials (link to them instead)
  • "What we learned" retrospectives

FORMAT

  • Use code blocks for paths and commands
  • Group related files under headers
  • Prefer tables over bullet lists for structured data
  • End with quick-reference block for scanning

Working with Existing Documents

When condensing an existing verbose doc:

  1. Identify the core purpose (first paragraph of output)
  2. Extract all file paths into grouped tables
  3. Preserve code samples that show patterns
  4. Convert prose sections to tables where possible
  5. Add single history reference line
  6. Verify no information loss on key technical details

Style Guide

See references/style-guide.md for detailed formatting rules, table patterns, and code sample guidelines.

Example Transformation

Before (verbose):

We have successfully achieved and EXCEEDED the original goals of this specification!
After many iterations and improvements, our automation rate reached 96.6% which is
above our target of 95%. The team worked hard on this and we're very proud...

After (concise):

**v31 PRODUCTION** | 96.6% automation (target: 95%)

Calibration Rules

  • Condensed output must not exceed 40% of source document length measured in words.
  • All file paths present in the original must appear in the condensed output — paths are never dropped.
  • Code blocks are never removed outright; reduce length by extracting the representative pattern and adding a source reference comment.

Error Handling

  • Source document has no clear purpose: ask for one sentence of context before condensing — do not infer a purpose and proceed.
  • A section contains items that are ambiguous between KEEP and REMOVE: default to KEEP and flag the section with a <!-- review: ambiguous --> comment in the output.
  • Condensed result loses required technical detail identified during verification: restore the omitted detail and re-measure against the 40% length cap; if the cap cannot be met, document the exception inline.
  • Source contains no file paths: skip the Implementation Files section entirely rather than generating placeholder paths.
  • Source is a non-text format (image, diagram, spreadsheet): report the format is unsupported and return without output.
  • Style guide conflicts with source formatting conventions: follow references/style-guide.md and note the override at the top of the output.

Limitations

  • Works on text documents only; images, diagrams, and binary files cannot be condensed.
  • Condensation ratio depends on source verbosity — highly structured sources yield less reduction.
  • Style guide deference (references/style-guide.md) takes precedence over source formatting, which can alter heading levels and table layouts.
  • Does not follow hyperlinks or fetch referenced external documents; referenced content is noted but not inlined.

平台分布

Codex

36.44%
按下载量换算88

Claude

33.38%
按下载量换算80

Cursor

18.39%
按下载量换算44

Gemini CLI

9.08%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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