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Agent Skill · 待分类

content-digest(内容摘要)

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。

content-digest 是待分类类 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,第三方扫描有提醒项,按安装量在待分类类的 11,209 个 Skill 里排第 4,124。

总安装 808周安装 34GitHub Stars 279下载 283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:content-digest(内容摘要)
来源仓库:https://github.com/zephyrwang6/myskill
仓库路径:skills/content-digest
安装命令:
npx skills add https://github.com/zephyrwang6/myskill --skill content-digest
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/zephyrwang6/myskill --skill content-digest

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

content-digest 用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。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。

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。

来源仓库内路径:skills/content-digest

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/zephyrwang6/myskill --skill content-digest;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

content-digest 怎么安装?

在终端执行 npx skills add https://github.com/zephyrwang6/myskill --skill content-digest。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

content-digest 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。

content-digest 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。

content-digest 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。

content-digest 用的人多吗?

公开数据是累计安装 808 次,近一周安装 34 次,下载 283 次,GitHub 279 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。

content-digest 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理待分类类任务、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。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
content-digest

内容摘要 · 本页

808279需要联网未明确有提醒

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

Content Digest

Transform long-form content into compelling short-form and long-form narratives.

Overview

This skill converts lengthy content (YouTube videos, podcasts, interviews, articles) into two distinct formats:

  1. Short-Form (短文): Social media-friendly summaries (300-800 characters) with numbered emoji lists (1️⃣2️⃣3️⃣)
  2. Long-Form (长文): Narrative articles (1500-3000+ characters) with story arcs, section headers, and integrated quotes

Workflow

1. Obtain the Content

If user provides a URL:

  • YouTube links: Use WebFetch or attempt to extract transcript
  • Article URLs: Use WebFetch to retrieve content
  • Podcast links: Fetch transcript if available

If user provides text:

  • Read the full transcript or article text directly

If content is unclear:

  • Ask: "Please provide the YouTube link, podcast transcript, or article you'd like me to transform."

2. Determine Output Format

If user specifies format:

  • Proceed with their choice (short-form only, long-form only, or both)

If user does not specify:

  • Ask: "Would you like: (1) Short-form only, (2) Long-form only, or (3) Both versions?"

Default behavior:

  • Generate both versions to maximize value

3. Deep Analysis - Four-Stage Process

CRITICAL: Follow this systematic process to ensure depth

Stage 1: Extract All Viewpoints (50+ minimum)

Read the entire content thoroughly and extract ALL viewpoints, including:

  • Explicit statements and opinions
  • Implicit beliefs revealed through stories
  • Decision-making rationales
  • Observations about the industry/domain
  • Personal experiences and lessons
  • Counterexamples and contrasts
  • Numbers, data points, specific examples

Goal: Create a comprehensive list of 50+ viewpoints before filtering. Don't judge quality yet - just extract everything.

Stage 2: Filter for Non-Consensus & Depth

From the 50+ viewpoints, identify and mark those that are:

  • Non-consensus (非共识): Challenges industry conventional wisdom
  • Personal/private insights (个人私下表达): Things people think but rarely say publicly
  • Counterintuitive (反直觉): Surprises even informed readers
  • Interesting trivia (有意思的冷知识): Specific details that reveal deeper patterns
  • Mental models: Frameworks that explain decision-making
  • Second-order insights: Not just "what" but "why this matters philosophically"
  • Paradoxes and tensions: Contradictions that expose underlying principles

Goal: Flag the 20-30 viewpoints that pass the "non-obvious test" - would a smart, informed reader already know this?

Stage 3: Select Core Narrative Elements

Identify:

  • Core narrative: What's the main story or theme?
  • Memorable quotes: Direct quotes that capture big ideas or reveal character
  • Turning points: Moments of realization or paradigm shifts
  • Dramatic elements: Irony, contrast, or unexpected outcomes
  • Specific details: Names, numbers, dates that prove the deeper point
Stage 4: Curate Final Insights

From the filtered viewpoints (Stage 2) and narrative elements (Stage 3):

  • For short-form: Select 10-15 most profound, actionable insights
  • For long-form: Use the same 10-15 insights as the foundation, then weave in narrative arc

4. Generate Short-Form Version

CRITICAL: Use ONLY the 10-15 curated insights from Stage 4

Consult style-guide.md for detailed guidelines. See examples.md for reference.

Structure:

# MMDD:[嘉宾名] X [栏目名]:[一句话核心观点]

今天看到 [嘉宾名] 去了 [栏目名] 的播客。

[嘉宾名] [2-3句话介绍嘉宾背景和核心成就,用具体数据]。

这期播客总共录了 [时长],[嘉宾名] 谈到了 [N] 个有趣的观点:

1、[观点标题/关键词]。[完整的逻辑阐述,包含推理过程,2-4句话]

2、[观点标题/关键词]。[完整的逻辑阐述,包含推理过程,2-4句话]

3、[观点标题/关键词]。[完整的逻辑阐述,包含推理过程,2-4句话]

...
[Continue with 10-15 total points]

---

标题格式 (重要):

  • 格式:# MMDD:嘉宾名 X 栏目名:一句话观点
  • 示例:# 0130:Peter Steinberger X The Pragmatic Engineer:一天600次提交,代码比以前更好
  • 一句话观点要抓住最反直觉或最有冲击力的点

开头格式 (重要):

  • 第一句:简单说看了什么 今天看到 [嘉宾] 去了 [栏目] 的播客。
  • 第二段:2-3句介绍嘉宾背景,必须有具体数据(数字、公司名、产品名)
  • 第三句:过渡句 这期播客总共录了 [时长],[嘉宾] 谈到了 [N] 个有趣的观点:
  • 如果不知道时长,可以写"将近两小时"或省略时长

List Format Rules (重要):

  • 使用数字+顿号格式 (1、 2、 3、...)

- 注意是顿号「、」不是点号「.」

  • 每条是完整段落,包含:

- 观点/结论(第一句) - 逻辑推理/因果解释(后续句子) - 具体例子或数据(如有)

  • 每条 2-4 句话,50-150 字
  • 观点要有深度:

- ✅ "AI 应用创业者不会相信 AGI。逻辑很简单,如果真信,就不应该做 AI 应用创业。AGI 如果存在,创业就只剩一件事:去做有机会达成 AGI 的模型。" - ❌ "AI 应用创业者不相信 AGI"(太简单,没有逻辑推理)

  • 保持 10-15 条(不是 8-12 条)

Key principles:

  • 标题要有冲击力,抓住最反直觉的观点
  • 开头简洁,快速进入正题
  • 每条观点要有完整的逻辑链条
  • 观点之间可以有递进或对比关系
  • Include specific data when available (numbers, names, percentages)
  • Pass the non-obvious test (would informed readers NOT already know this?)
  • End with separator line ---

5. Generate Long-Form Version

CRITICAL: Build ENTIRELY on the same 10-15 curated insights from Stage 4

The long-form version is NOT a separate summary - it's a narrative expansion of the SHORT-form insights with story arc and analytical depth.

Consult style-guide.md for detailed guidelines. See examples.md for reference.

IMPORTANT: Choose the right style based on content type

For interview/podcast/dialogue content → Use Style B (对话式访谈) For solo speech/article/essay → Use Style A (叙事性文章)


Style A Structure (叙事性文章):

[Compelling Title - derived from core insight]

[Opening: Set the scene using one of the 10-15 insights]

### [Section 1: Background]
"[Key quote]"
[Context - connect to 2-3 of your curated insights]

### [Section 2: Main Content]
[Narrative development - weave in 4-5 curated insights with quotes and analysis]

### [Section 3: Climax]
[Dramatic highlights - reveal most counterintuitive insight]

### [Section 4: Resolution/Turning Point]
"[Pivotal quote]"
[Significance - tie back to mental model or principle]

### [Epilogue: Reflection]
[What happened after / Ironic contrast - connect final insights]

[Optional: Source attribution]

Key principles (Style A):

  • Compelling title derived from your deepest insight
  • Clear section headers for navigation
  • Each section develops 2-4 of your 10-15 curated insights with narrative and quotes
  • Integrate direct quotes naturally - use them to prove your curated insights
  • Build narrative arc: setup → development → climax → resolution
  • Use dramatic irony when relevant ("他不知道的是..." / "He didn't know that...")
  • Include specific details (names, numbers, dates) from your curated list
  • Deep analytical layer - weave in your Stage 2 filtered insights:

- Why specific choices reveal broader strategic principles - How contradictions or tensions expose underlying philosophies - What the subject's evolution teaches about the domain - Connections between micro-decisions and macro-outcomes

  • 2000-3500+ characters to properly develop 10-15 deep insights

Style B Structure (对话式访谈):

[前言/导语 - 编辑者视角]
断断续续,终于看完了...[个人感受]
干货很多。[嘉宾]可能是...[定位评价]
[为什么值得关注]
我今天不忙,把这次访谈全文精编出来,供大家学习。赠人玫瑰,手有余香。
[可选:节日祝福]
下面是 YouTube/播客链接:[链接]

#01 [主题标题 - 简短有力]
主持人:[问题]
嘉宾:[回答 - 保留对话感]

[编辑补充:数据解读、背景、个人观点]
[可用:"我觉得这个点真的太重要了" "这太有意思了"]

主持人:[追问]
嘉宾:[深入回答]

[继续分析]
[可选:插入相关文章链接]

#02 [第二主题]
主持人:[新话题]
嘉宾:[回答]

[编辑解读]
...

#03-#0N [按主题继续]
...

Key principles (Style B):

  • 口语化开场:"断断续续看完" "干货很多" 体现真实感
  • 编号主题:用 #01 #02 等清晰分段,主题标题直白有力
  • 保留对话:60-70%保持"主持人:""嘉宾:"格式
  • 编辑介入:20-30%加入编辑分析、补充、个人反应
  • 口语化表达:"太离谱了" "我觉得" "说实话" "天哪" "完全是这样"
  • 具体数据:必须保留数字、人名、公司名
  • 补充链接:适时插入"文章链接:..." 延伸阅读
  • 人情味结尾:"赠人玫瑰,手有余香"
  • 主题重组:不按时间线,按话题逻辑重新组织
  • 3000-8000+ 字(根据访谈长度)

6. Quality Check

Before delivering, verify you followed the four-stage process:

Stage 1 verification:

  • Extracted 50+ viewpoints from source material (can be implicit - doesn't need to be shown to user)

Stage 2 verification:

  • Filtered for non-consensus, counterintuitive, and deep insights
  • Identified personal/private expressions and interesting trivia
  • Marked mental models and second-order insights

Stage 3 & 4 verification:

  • Selected 10-15 most profound insights for final output
  • Short version uses ALL 10-15 curated insights
  • Long version develops the SAME 10-15 insights with narrative

Depth check - Each of the 10-15 insights must:

  • Pass the "non-obvious test": Would an informed reader already know this?
  • Reveal a mental model, framework, or underlying principle
  • Challenge conventional thinking OR expose interesting trivia
  • Connect ideas in an unexpected way OR show second-order effects

Quality verification:

  • Quotes are accurate and attributed
  • No editorializing beyond source material
  • Writing is engaging, not robotic
  • Both versions can stand alone
  • Numbers/facts are specific, not vague
  • The "so what?" is clear to readers
  • Every takeaway reveals WHY it matters, not just WHAT happened
  • Short and long versions share the same insight foundation

7. Deliver Output

文件保存规范:

  • 保存位置:/Users/ugreen/Documents/obsidian/每日播客/
  • 文件命名:MMDD-主题关键词.md

- MMDD 为当天日期(如 0109 表示 1 月 9 日) - 主题关键词 为 2-6 个字的内容概括(如 Lovable增长策略、AI编程工具) - 示例:0109-Lovable增长策略.md、0108-睡眠科学.md

  • 自动保存:生成内容后,使用 Write 工具将完整内容保存到上述路径

格式规范:

  • 行距:段落内不留空行,段落之间留一个空行
  • 文档标题:使用 # MMDD:嘉宾名 X 栏目名:一句话观点 格式
  • 长文标题:使用 # 精华片段

结构模板:

# MMDD:[嘉宾名] X [栏目名]:[一句话核心观点]

今天看到 [嘉宾名] 去了 [栏目名] 的播客。

[嘉宾名] [2-3句话介绍嘉宾背景,用具体数据]。

这期播客总共录了 [时长],[嘉宾名] 谈到了 [N] 个有趣的观点:

1、[观点]。[逻辑推理]

2、[观点]。[逻辑推理]

...(共 10-15 条)

---

# 精华片段

[长文内容 - Style B 对话式访谈格式]

---

[结束语]

开场介绍模板(新格式):

# 0130:Peter Steinberger X The Pragmatic Engineer:一天600次提交,代码比以前更好

今天看到 Peter Steinberger 去了 The Pragmatic Engineer 的播客。

Peter Steinberger 创建了 PSPDF kit——一个被超过 10 亿设备使用的 PDF 框架,后来经历严重职业倦怠卖掉股份消失了 3 年。2024 年回归后,他用完全不同的方式创建了 Clawdbot,一周从 100 星涨到 3300 星。

这期播客总共录了将近两小时,Peter 谈到了 11 个有趣的观点:

观点列表示例:

1、AI 应用创业者不会相信 AGI。逻辑很简单,如果真信,就不应该做 AI 应用创业。AGI 如果存在,创业就只剩一件事:去做有机会达成 AGI 的模型。

2、创业的产品定位,关键点之一是:要做工作流的上游。上游的产品,不容易被下游的产品吃掉,反过来,发展到一定阶段,上游可以做下游的事。

3、Code Reviews 已死。对 prompts 的兴趣比代码更大,PR 应该叫"Prompt Requests"。因为 prompt 给的信号更高——你是怎么得到这个解决方案的?问了什么?有多少引导?

结束语模板:

---

[嘉宾名字]凭[具体贡献],[产生的影响]。

用他的话收个尾:"[引用一句有力的话]"。[最后一句个人感受或升华]。

YouTube 链接:[链接]

If only one format was requested, still include opening and closing sections.

Core Principles

Extract Strategically

What to extract:

  • Deep, counterintuitive insights - not surface observations:

- Mental models and frameworks that drive decision-making - Paradoxes and tensions that reveal underlying principles - Second-order effects and non-obvious consequences - Patterns that connect specific tactics to strategic outcomes

  • Surprising insights that challenge common wisdom
  • Practical wisdom with WHY - not just "do X" but "X reveals principle Y"
  • Memorable quotes capturing big ideas or philosophical stances
  • Turning points and paradigm shifts
  • Human moments (vulnerability, humor, authenticity) that reveal character
  • Contextual ironies (what they didn't know then vs. now)

What to avoid:

  • Linear summarization without insight
  • Including everything (be selective)
  • Stating the obvious - if a reasonably informed reader would already know it, dig deeper
  • Surface-level descriptions without explaining WHY it matters
  • Losing the human voice
  • Adding information not in source
  • AI-style generic phrasing
  • Shallow takeaways - "X did Y" without revealing what principle or framework this demonstrates

Voice & Tone

  • Conversational but insightful: Like explaining to a smart friend
  • Show, don't tell: Use quotes to prove points
  • Respect the source: Don't editorialize or distort
  • Find the story: Every piece has a narrative arc

Be Token-Efficient

This skill focuses on creative transformation, not code execution. The writing process happens in-context without requiring scripts.

Resources

references/style-guide.md

Detailed writing guidelines for both short-form and long-form styles, including:

  • Structure patterns
  • Key characteristics
  • Writing principles
  • What to avoid
  • Extraction strategies

Load this when you need detailed guidance on tone, structure, or style.

references/examples.md

Complete reference examples:

  • Short-form example: Boris Cherny's Claude Code workflow
  • Long-form example: Manus/Peak Ji interview article

Load this when you need concrete examples of the final output quality and style.

Notes

  • Both styles require full comprehension of source material - don't skim
  • Short-form emphasizes actionable takeaways
  • Long-form emphasizes narrative and character
  • Quotes must be accurate and in context
  • Works best with content that has inherent narrative or insight
  • Can combine with translation if source is in different language
  • Ideal for content creators repurposing long content for different platforms

平台分布

Codex

33.54%
按下载量换算95

Claude

31.14%
按下载量换算88

Cursor

17.28%
按下载量换算49

Gemini CLI

8.36%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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