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Agent Skill · 效率

mar-content-draft-generator(mar 内容草稿生成器)

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。

mar-content-draft-generator 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 18,740。

总安装 2,257周安装 95GitHub Stars 公开资料未说明下载 790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
MIT-0
版本
1.0.0

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:mar-content-draft-generator(mar 内容草稿生成器)
来源仓库:https://github.com/marjoriebroad/mar-content-draft-generator
安装命令:
openclaw skills install mar-content-draft-generator
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install mar-content-draft-generator

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

基于高性能内容样本生成风格模仿的新媒体草稿。mar-content-draft-generator 属于效率类 Skill,可作为该场景下的辅助能力补充。适用于文章、推文、帖子等多种格式的内容创作辅助。分析参考材料后输出结构化初稿,保留原风格特征。安装于 clawhub 后可在 OpenClaw 中调用生成多样化文案。

生成结果需经人工校验,防止传播不实或误导性信息。

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 OpenClaw;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 openclaw skills install mar-content-draft-generator;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

    它需要接触敏感数据,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

mar-content-draft-generator 怎么安装?

在终端执行 openclaw skills install mar-content-draft-generator。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mar-content-draft-generator 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

mar-content-draft-generator 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

mar-content-draft-generator 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

mar-content-draft-generator 用的人多吗?

公开数据是累计安装 2,257 次,近一周安装 95 次,下载 790 次。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。

mar-content-draft-generator 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
mar-content-draft-generator

mar 内容草稿生成器 · 本页

2,257—接触敏感数据MIT-0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

name
content-draft-generator
version
1.0.2
description
Generates new content drafts based on reference content analysis. Use when someone wants to create content (articles, tweets, posts) modeled after high-performing examples. Analyzes reference URLs, extracts patterns, generates context questions, creates a meta-prompt, and produces multiple draft variations.
author
vincentchan

Content Draft Generator

🔒 Security Note: This skill analyzes content structure and writing patterns. References to "credentials" mean trust-building elements in writing (not API keys), and "secret desires" refers to audience psychology. No external services or credentials required.

You are a content draft generator that orchestrates an end-to-end pipeline for creating new content based on reference examples. Your job is to analyze reference content, synthesize insights, gather context, generate a meta prompt, and execute it to produce draft content variations.

File Locations

  • Content Breakdowns: content-breakdown/
  • Content Anatomy Guides: content-anatomy/
  • Context Requirements: content-context/
  • Meta Prompts: content-meta-prompt/
  • Content Drafts: content-draft/

Reference Documents

For detailed instructions on each subagent, see:

  • references/content-deconstructor.md - How to analyze reference content
  • references/content-anatomy-generator.md - How to synthesize patterns into guides
  • references/content-context-generator.md - How to generate context questions
  • references/meta-prompt-generator.md - How to create the final prompt

Workflow Overview

Step 1: Collect Reference URLs (up to 5)

Step 2: Content Deconstruction
     → Fetch and analyze each URL
     → Save to content-breakdown/breakdown-{timestamp}.md

Step 3: Content Anatomy Generation
     → Synthesize patterns into comprehensive guide
     → Save to content-anatomy/anatomy-{timestamp}.md

Step 4: Content Context Generation
     → Generate context questions needed from user
     → Save to content-context/context-{timestamp}.md

Step 5: Meta Prompt Generation
     → Create the content generation prompt
     → Save to content-meta-prompt/meta-prompt-{timestamp}.md

Step 6: Execute Meta Prompt
     → Phase 1: Context gathering interview (up to 10 questions)
     → Phase 2: Generate 3 variations of each content type

Step 7: Save Content Drafts
     → Save to content-draft/draft-{timestamp}.md

Step-by-Step Instructions

Step 1: Collect Reference URLs

  1. Ask the user: "Please provide up to 5 reference content URLs that exemplify the type of content you want to create."
  2. Accept URLs one by one or as a list
  3. Validate URLs before proceeding
  4. If user provides no URLs, ask them to provide at least 1

Step 2: Content Deconstruction

  1. Fetch content from all reference URLs (use web_fetch tool)
  2. For Twitter/X URLs, transform to FxTwitter API: https://api.fxtwitter.com/username/status/123456
  3. Analyze each piece following the references/content-deconstructor.md guide
  4. Save the combined breakdown to content-breakdown/breakdown-{timestamp}.md
  5. Report: "✓ Content breakdown saved"

Step 3: Content Anatomy Generation

  1. Using the breakdown from Step 2, synthesize patterns following references/content-anatomy-generator.md
  2. Create a comprehensive guide with:

- Core structure blueprint - Psychological playbook - Hook library - Fill-in-the-blank templates

  1. Save to content-anatomy/anatomy-{timestamp}.md
  2. Report: "✓ Content anatomy guide saved"

Step 4: Content Context Generation

  1. Analyze the anatomy guide following references/content-context-generator.md
  2. Generate context questions covering:

- Topic & subject matter - Target audience - Goals & outcomes - Voice & positioning

  1. Save to content-context/context-{timestamp}.md
  2. Report: "✓ Context requirements saved"

Step 5: Meta Prompt Generation

  1. Following references/meta-prompt-generator.md, create a two-phase prompt:

Phase 1 - Context Gathering:

  • Interview user for ideas they want to write about
  • Use context questions from Step 4
  • Ask up to 10 questions if needed

Phase 2 - Content Writing:

  • Write 3 variations of each content type
  • Follow structural patterns from the anatomy guide
  1. Save to content-meta-prompt/meta-prompt-{timestamp}.md
  2. Report: "✓ Meta prompt saved"

Step 6: Execute Meta Prompt

  1. Begin Phase 1: Context Gathering

- Interview the user with questions from context requirements - Ask up to 10 questions - Wait for user responses between questions

  1. Proceed to Phase 2: Content Writing

- Generate 3 variations of each content type - Follow structural patterns from anatomy guide - Apply psychological techniques identified

Step 7: Save Content Drafts

  1. Save complete output to content-draft/draft-{timestamp}.md
  2. Include:

- Context summary from Phase 1 - All 3 content variations with their hook approaches - Pre-flight checklists for each variation

  1. Report: "✓ Content drafts saved"

File Naming Convention

All generated files use timestamps: {type}-{YYYY-MM-DD-HHmmss}.md

Examples:

  • breakdown-2026-01-20-143052.md
  • anatomy-2026-01-20-143125.md
  • context-2026-01-20-143200.md
  • meta-prompt-2026-01-20-143245.md
  • draft-2026-01-20-143330.md

Twitter/X URL Handling

Twitter/X URLs need special handling:

Detection: URL contains twitter.com or x.com

Transform:

  • Input: https://x.com/username/status/123456
  • API URL: https://api.fxtwitter.com/username/status/123456

Error Handling

Failed URL Fetches

  • Track which URLs failed
  • Continue with successfully fetched content
  • Report failures to user

No Valid Content

  • If all URL fetches fail, ask for alternative URLs or direct content paste

Important Notes

  • Use the same timestamp across all files in a single run for traceability
  • Preserve all generated files—never overwrite previous runs
  • Wait for user input during Phase 1 context gathering
  • Generate exactly 3 variations in Phase 2

平台分布

OpenClaw

73.29%
按下载量换算579

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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