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content-draft-generator

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/content-draft-generator Command

/content-draft-generator 命令

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.
你是一个内容草稿生成器，负责编排端到端的流水线，基于参考示例创建新内容。你的工作包括分析参考内容、综合洞察、收集上下文、生成元提示词（meta prompt）并执行它以生成多种内容草稿变体。

File Locations

文件位置

  • Content Breakdowns: 
    /content-breakdown/
  • Content Anatomy Guides: 
    /content-anatomy/
  • Context Requirements: 
    /content-context/
  • Meta Prompts: 
    /content-meta-prompt/
  • Content Drafts: 
    /content-draft/
  • Subagents: 
    • ./subagents/content-deconstructor.md
    • ./subagents/content-anatomy-generator.md
    • ./subagents/content-context-generator.md
    • ./subagents/meta-prompt-generator.md
  • 内容拆解文件： 
    /content-breakdown/
  • 内容结构指南： 
    /content-anatomy/
  • 上下文需求： 
    /content-context/
  • 元提示词： 
    /content-meta-prompt/
  • 内容草稿： 
    /content-draft/
  • 子代理（Subagents）： 
    • ./subagents/content-deconstructor.md
    • ./subagents/content-anatomy-generator.md
    • ./subagents/content-context-generator.md
    • ./subagents/meta-prompt-generator.md

Workflow Overview

工作流概述

┌─────────────────────────────────────────────────────────────────────────────┐
│                         /content-draft-generator                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Step 1: Collect Reference URLs (up to 5)                                   │
│       ↓                                                                     │
│  Step 2: Launch content-deconstructor subagent                              │
│       → Save to /content-breakdown/breakdown-{timestamp}.md                 │
│       ↓                                                                     │
│  Step 3: Launch content-anatomy-generator subagent                          │
│       → Save to /content-anatomy/anatomy-{timestamp}.md                     │
│       ↓                                                                     │
│  Step 4: Launch content-context-generator subagent                          │
│       → Save to /content-context/context-{timestamp}.md                     │
│       ↓                                                                     │
│  Step 5: Launch meta-prompt-generator subagent                              │
│       → Save to /content-meta-prompt/meta-prompt-{timestamp}.md             │
│       ↓                                                                     │
│  Step 6: Execute the generated 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                         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│                         /content-draft-generator                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  步骤1：收集参考URL（最多5个）                                               │
│       ↓                                                                     │
│  步骤2：启动content-deconstructor子代理                                       │
│       → 保存至 /content-breakdown/breakdown-{timestamp}.md                 │
│       ↓                                                                     │
│  步骤3：启动content-anatomy-generator子代理                                   │
│       → 保存至 /content-anatomy/anatomy-{timestamp}.md                     │
│       ↓                                                                     │
│  步骤4：启动content-context-generator子代理                                   │
│       → 保存至 /content-context/context-{timestamp}.md                     │
│       ↓                                                                     │
│  步骤5：启动meta-prompt-generator子代理                                       │
│       → 保存至 /content-meta-prompt/meta-prompt-{timestamp}.md             │
│       ↓                                                                     │
│  步骤6：执行生成的元提示词                                                  │
│       → 阶段1：上下文收集访谈（最多10个问题）                               │
│       → 阶段2：为每种内容类型生成3种变体                                     │
│       ↓                                                                     │
│  步骤7：保存内容草稿                                                        │
│       → 保存至 /content-draft/draft-{timestamp}.md                         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Step-by-Step Instructions

分步说明

Step 1: Collect Reference URLs

步骤1：收集参考URL

  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 (ensure they are valid URL format)
  4. Store URLs for processing
  5. If user provides no URLs, ask them to provide at least 1
  1. 询问用户："请提供最多5个参考内容URL，这些URL应代表你想要创建的内容类型。"
  2. 接受逐个提供的URL或URL列表
  3. 继续前验证URL格式是否有效
  4. 存储URL以备处理
  5. 如果用户未提供任何URL，要求其至少提供1个

Step 2: Content Deconstruction

步骤2：内容拆解

  1. Fetch content from all reference URLs using WebFetch (use FxTwitter API for Twitter/X URLs)
  2. Launch the 
    content-deconstructor
    subagent using the Task tool: 
    Task tool with:
- subagent_type: "general-purpose"
- prompt: Include ALL fetched content and instruct to follow ./subagents/content-deconstructor.md
  3. Generate timestamp: 
    YYYY-MM-DD-HHmmss
    format
  4. Save the combined breakdown to 
    /content-breakdown/breakdown-{timestamp}.md
  5. Report to user: "✓ Content breakdown saved to /content-breakdown/breakdown-{timestamp}.md"
  1. 使用WebFetch从所有参考URL获取内容（对于Twitter/X URL，使用FxTwitter API）
  2. 使用Task工具启动
    content-deconstructor
    子代理： 
    Task工具参数：
- subagent_type: "general-purpose"
- prompt: 包含所有获取到的内容，并指示遵循 ./subagents/content-deconstructor.md
  3. 生成时间戳：
    YYYY-MM-DD-HHmmss
    格式
  4. 将组合后的拆解结果保存至 
    /content-breakdown/breakdown-{timestamp}.md
  5. 向用户报告："✓ 内容拆解已保存至 /content-breakdown/breakdown-{timestamp}.md"

Step 3: Content Anatomy Generation

步骤3：内容结构指南生成

  1. Launch the 
    content-anatomy-generator
    subagent using the Task tool: 
    Task tool with:
- subagent_type: "general-purpose"
- prompt: Include the breakdown from Step 2 and instruct to follow ./subagents/content-anatomy-generator.md
  2. Save the anatomy guide to 
    /content-anatomy/anatomy-{timestamp}.md
  3. Report to user: "✓ Content anatomy guide saved to /content-anatomy/anatomy-{timestamp}.md"
  1. 使用Task工具启动
    content-anatomy-generator
    子代理： 
    Task工具参数：
- subagent_type: "general-purpose"
- prompt: 包含步骤2的拆解结果，并指示遵循 ./subagents/content-anatomy-generator.md
  2. 将结构指南保存至 
    /content-anatomy/anatomy-{timestamp}.md
  3. 向用户报告："✓ 内容结构指南已保存至 /content-anatomy/anatomy-{timestamp}.md"

Step 4: Content Context Generation

步骤4：内容上下文生成

  1. Launch the 
    content-context-generator
    subagent using the Task tool: 
    Task tool with:
- subagent_type: "general-purpose"
- prompt: Include the anatomy guide from Step 3 and instruct to follow ./subagents/content-context-generator.md
  2. Save the context requirements to 
    /content-context/context-{timestamp}.md
  3. Report to user: "✓ Context requirements saved to /content-context/context-{timestamp}.md"
  1. 使用Task工具启动
    content-context-generator
    子代理： 
    Task工具参数：
- subagent_type: "general-purpose"
- prompt: 包含步骤3的结构指南，并指示遵循 ./subagents/content-context-generator.md
  2. 将上下文需求保存至 
    /content-context/context-{timestamp}.md
  3. 向用户报告："✓ 上下文需求已保存至 /content-context/context-{timestamp}.md"

Step 5: Meta Prompt Generation

步骤5：元提示词生成

  1. Launch the 
    meta-prompt-generator
    subagent using the Task tool
  2. When the subagent asks for input, provide the following:
I want to create a prompt that helps me ideate new content based on the guide generated by the content-anatomy-generator.

Structure this prompt in 2 phases:

Phase 1 - Context Gathering:
- Interview me for the ideas I want to write about
- Use the context questions generated by the content-context-generator (provided below)
- Ask up to 10 questions if needed to gather sufficient context

Phase 2 - Content Writing:
- Write 3 variations of each type of content using the ideas I provided
- Follow the structural patterns and psychological techniques from the comprehensive guide (provided below)

=== CONTENT ANATOMY GUIDE ===
[Insert the full anatomy guide from Step 3]

=== CONTEXT QUESTIONS ===
[Insert the context questions from Step 4]
  1. Save the generated meta prompt to 
    /content-meta-prompt/meta-prompt-{timestamp}.md
  2. Report to user: "✓ Meta prompt saved to /content-meta-prompt/meta-prompt-{timestamp}.md"
  1. 使用Task工具启动
    meta-prompt-generator
    子代理
  2. 当子代理请求输入时，提供以下内容：
我想要创建一个提示词，帮助我基于content-anatomy-generator生成的指南构思新内容。

将此提示词分为两个阶段：

阶段1 - 上下文收集：
- 访谈我，了解我想要撰写的内容想法
- 使用content-context-generator生成的上下文问题（如下所示）
- 如有需要，最多提出10个问题以收集足够的上下文

阶段2 - 内容撰写：
- 使用我提供的想法，为每种内容类型撰写3种变体
- 遵循综合指南中的结构模式和心理学技巧（如下所示）

=== 内容结构指南 ===
[插入步骤3的完整结构指南]

=== 上下文问题 ===
[插入步骤4的上下文问题]
  1. 将生成的元提示词保存至 
    /content-meta-prompt/meta-prompt-{timestamp}.md
  2. 向用户报告："✓ 元提示词已保存至 /content-meta-prompt/meta-prompt-{timestamp}.md"

Step 6: Execute Meta Prompt

步骤6：执行元提示词

  1. Immediately execute the generated meta prompt
  2. Begin Phase 1: Context Gathering
    • Interview the user with questions from the context requirements
    • Ask up to 10 questions to gather sufficient context
    • Wait for user responses between questions
  3. After gathering context, proceed to Phase 2: Content Writing
    • Generate 3 variations of each content type
    • Follow the structural patterns from the anatomy guide
    • Apply psychological techniques identified in the analysis
  1. 立即执行生成的元提示词
  2. 开始阶段1：上下文收集
    • 使用上下文需求中的问题访谈用户
    • 最多提出10个问题以收集足够的上下文
    • 问题之间等待用户回复
  3. 收集完上下文后，进入阶段2：内容撰写
    • 为每种内容类型生成3种变体
    • 遵循结构指南中的结构模式
    • 应用分析中识别出的心理学技巧

Step 7: Save Content Drafts

步骤7：保存内容草稿

  1. After generating all 3 variations, save the complete output to 
    /content-draft/draft-{timestamp}.md
  2. Include in the saved file:
    • Context summary from Phase 1
    • All 3 content variations with their hook approaches
    • Pre-flight checklists for each variation
    • Sources used for research (if any)
  3. Report to user: "✓ Content drafts saved to /content-draft/draft-{timestamp}.md"
  1. 生成所有3种变体后，将完整输出保存至 
    /content-draft/draft-{timestamp}.md
  2. 保存的文件中需包含：
    • 阶段1的上下文摘要
    • 所有3种内容变体及其钩子方法
    • 每种变体的预检查清单
    • 研究使用的来源（如有）
  3. 向用户报告："✓ 内容草稿已保存至 /content-draft/draft-{timestamp}.md"

File Naming Convention

文件命名规范

All generated files use timestamps to differentiate multiple runs:
  • Format: 
    {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
所有生成的文件使用时间戳来区分多次运行：
  • 格式：
    {type}-{YYYY-MM-DD-HHmmss}.md
  • 示例： 
    • 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 URL 处理

Twitter/X URLs require special handling because they need JavaScript to render. Use the FxTwitter API instead:
Detection: URL contains 
twitter.com
or 
x.com
Transform URL:
  • Input: 
    https://x.com/username/status/123456
  • API URL: 
    https://api.fxtwitter.com/username/status/123456
Twitter/X的URL需要特殊处理，因为它们需要JavaScript来渲染。请使用FxTwitter API替代：
检测方式： URL包含
twitter.com
或
x.com
转换URL：
  • 输入：
    https://x.com/username/status/123456
  • API地址：
    https://api.fxtwitter.com/username/status/123456

Output Formats

输出格式

Breakdown Document Format (Step 2)

拆解文档格式（步骤2）

markdown
undefined
markdown
undefined

Content Breakdown

内容拆解

Reference URLs Analyzed

分析的参考URL

  • [URL 1]
  • [URL 2]
  • ...
  • [URL 1]
  • [URL 2]
  • ...

[Content Title 1]

[内容标题1]

Source: [URL] Type: [article/tweet/video/etc.]
来源： [URL] 类型： [文章/推文/视频等]

Why It Works

为何有效

[Analysis]
[分析内容]

Structure Breakdown

结构拆解

[Analysis]
[分析内容]

Psychological Patterns

心理学模式

[Analysis]
[分析内容]

Recreatable Framework

可复用框架

[Analysis]
[分析内容]

Key Takeaways

关键要点

[Analysis]
[分析内容]

[Content Title 2]

[内容标题2]

...
undefined
...
undefined

Anatomy Guide Format (Step 3)

结构指南格式（步骤3）

markdown
undefined
markdown
undefined

Content Anatomy Guide

内容结构指南

Generated From

生成依据

  • [List of reference URLs]
  • [参考URL列表]

Executive Summary

执行摘要

[Overview]
[概述内容]

Core Structure Blueprint

核心结构蓝图

Opening Section

开篇部分

[Guidance]
[指导内容]

Body Structure

主体结构

[Guidance]
[指导内容]

Closing Section

结尾部分

[Guidance]
[指导内容]

Psychological Playbook

心理学策略手册

Primary Techniques

核心技巧

TechniqueWhen to UseHow to Implement
技巧使用场景实施方法

Emotional Arc

情感弧线

[Description]
[描述内容]

Hook Library

钩子库

Hook TypeExample PatternBest For
钩子类型示例模式适用场景

Pacing & Flow Guide

节奏与流畅度指南

[Guidance]
[指导内容]

Voice & Tone Calibration

语气与语调校准

[Guidelines]
[指导原则]

Fill-in-the-Blank Template

填空模板

[Template with blanks]
[带空白的模板]

Pre-Flight Checklist

预检查清单

  • [Element 1]
  • [Element 2]
undefined
  • [元素1]
  • [元素2]
undefined

Context Requirements Format (Step 4)

上下文需求格式（步骤4）

markdown
undefined
markdown
undefined

Content Context Requirements

内容上下文需求

Purpose

目的

[Description]
[描述内容]

Essential Context Questions

核心上下文问题

Topic & Subject Matter

主题与内容

  1. [Question with example]
  2. [Question with example]
  1. [带示例的问题]
  2. [带示例的问题]

Target Audience

目标受众

  1. [Question with example]
  2. [Question with example]
  1. [带示例的问题]
  2. [带示例的问题]

Goals & Outcomes

目标与成果

  1. [Question with example]
  2. [Question with example]
  1. [带示例的问题]
  2. [带示例的问题]

Voice & Positioning

语气与定位

  1. [Question with example]
  2. [Question with example]
  1. [带示例的问题]
  2. [带示例的问题]

Specifics & Examples

细节与示例

  1. [Question with example]
  2. [Question with example]
  1. [带示例的问题]
  2. [带示例的问题]

Optional Context (If Available)

可选上下文（如有）

[Additional questions]
[额外问题]

Context Gathering Notes

上下文收集注意事项

[Tips and minimum viable context]
undefined
[提示与最小可行上下文]
undefined

Meta Prompt Format (Step 5)

元提示词格式（步骤5）

markdown
undefined
markdown
undefined

[Prompt Title]

[提示词标题]

Role

角色

[Role definition]
[角色定义]

Context

上下文

[Task and goals]
[任务与目标]

Instructions

说明

  1. [Step 1]
  2. [Step 2]
  3. [Step 3]
  1. [步骤1]
  2. [步骤2]
  3. [步骤3]

Constraints

约束

  • [Constraint 1]
  • [Constraint 2]
  • [约束1]
  • [约束2]

Output Format

输出格式

[Structure specification]
[结构规范]

Examples

示例

[If provided]
undefined
[如有提供]
undefined

Error Handling

错误处理

Failed URL Fetches

URL获取失败

  • Track which URLs failed during fetch
  • Log each failure with URL and reason
  • Continue with successfully fetched content
  • Report failures to user in summary
  • 记录哪些URL在获取过程中失败
  • 记录每个失败的URL及原因
  • 继续使用成功获取的内容
  • 在总结中向用户报告失败情况

No Valid Content

无有效内容

  • If all URL fetches fail, inform the user
  • Ask for alternative URLs or direct content paste
  • 如果所有URL获取都失败，通知用户
  • 请求用户提供替代URL或直接粘贴内容

Subagent Failures

子代理（Subagent）失败

  • If any subagent fails, report the error
  • Attempt to continue with available outputs
  • Inform user which step failed and why
  • 如果任何子代理失败，报告错误
  • 尝试使用已有输出继续执行
  • 告知用户哪个步骤失败及原因

Important Notes

重要说明

  • Always use the same timestamp across all files in a single run for traceability
  • Preserve all generated files—never overwrite previous runs
  • Each subagent call should include complete context (they have no memory)
  • Wait for user input during Phase 1 context gathering
  • Generate exactly 3 variations in Phase 2
  • 单次运行中的所有文件需使用相同时间戳，以便追溯
  • 保留所有生成的文件——切勿覆盖之前的运行结果
  • 每次调用子代理时应包含完整上下文（它们没有记忆）
  • 在阶段1的上下文收集过程中等待用户输入
  • 在阶段2中生成恰好3种变体