home / skills / vincentchan / ai-content-engine / content-draft-generator
This skill helps you generate structured content drafts by analyzing references, generating a meta prompt, and delivering three polished variations.
npx playbooks add skill vincentchan/ai-content-engine --skill content-draft-generatorReview the files below or copy the command above to add this skill to your agents.
---
name: content-draft-generator
description: Generates new content drafts based on reference content analysis
disable-model-invocation: true
---
# /content-draft-generator Command
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/`
- **Subagents:**
- `./subagents/content-deconstructor.md`
- `./subagents/content-anatomy-generator.md`
- `./subagents/content-context-generator.md`
- `./subagents/meta-prompt-generator.md`
## Workflow Overview
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ /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 │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
## 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 (ensure they are valid URL format)
4. Store URLs for processing
5. If user provides no URLs, ask them to provide at least 1
### Step 2: Content Deconstruction
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"
### Step 3: Content Anatomy Generation
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"
### Step 4: Content Context Generation
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"
### Step 5: Meta Prompt Generation
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]
```
3. Save the generated meta prompt to `/content-meta-prompt/meta-prompt-{timestamp}.md`
4. Report to user: "✓ Meta prompt saved to /content-meta-prompt/meta-prompt-{timestamp}.md"
### Step 6: Execute Meta Prompt
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
### Step 7: Save Content Drafts
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"
## File Naming Convention
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`
## Twitter/X URL Handling
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`
## Output Formats
### Breakdown Document Format (Step 2)
```markdown
# Content Breakdown
## Reference URLs Analyzed
- [URL 1]
- [URL 2]
- ...
---
## [Content Title 1]
**Source:** [URL]
**Type:** [article/tweet/video/etc.]
### Why It Works
[Analysis]
### Structure Breakdown
[Analysis]
### Psychological Patterns
[Analysis]
### Recreatable Framework
[Analysis]
### Key Takeaways
[Analysis]
---
## [Content Title 2]
...
```
### Anatomy Guide Format (Step 3)
```markdown
# Content Anatomy Guide
## Generated From
- [List of reference URLs]
## Executive Summary
[Overview]
## Core Structure Blueprint
### Opening Section
[Guidance]
### Body Structure
[Guidance]
### Closing Section
[Guidance]
## Psychological Playbook
### Primary Techniques
| Technique | When to Use | How to Implement |
|-----------|-------------|------------------|
### Emotional Arc
[Description]
## Hook Library
| Hook Type | Example Pattern | Best For |
|-----------|-----------------|----------|
## Pacing & Flow Guide
[Guidance]
## Voice & Tone Calibration
[Guidelines]
## Fill-in-the-Blank Template
[Template with blanks]
## Pre-Flight Checklist
- [ ] [Element 1]
- [ ] [Element 2]
```
### Context Requirements Format (Step 4)
```markdown
# Content Context Requirements
## Purpose
[Description]
## Essential Context Questions
### Topic & Subject Matter
1. [Question with example]
2. [Question with example]
### Target Audience
3. [Question with example]
4. [Question with example]
### Goals & Outcomes
5. [Question with example]
6. [Question with example]
### Voice & Positioning
7. [Question with example]
8. [Question with example]
### Specifics & Examples
9. [Question with example]
10. [Question with example]
## Optional Context (If Available)
[Additional questions]
## Context Gathering Notes
[Tips and minimum viable context]
```
### Meta Prompt Format (Step 5)
```markdown
# [Prompt Title]
## Role
[Role definition]
## Context
[Task and goals]
## Instructions
1. [Step 1]
2. [Step 2]
3. [Step 3]
## Constraints
- [Constraint 1]
- [Constraint 2]
## Output Format
[Structure specification]
## Examples
[If provided]
```
## Error Handling
### Failed URL Fetches
- Track which URLs failed during fetch
- Log each failure with URL and reason
- Continue with successfully fetched content
- Report failures to user in summary
### No Valid Content
- If all URL fetches fail, inform the user
- Ask for alternative URLs or direct content paste
### Subagent Failures
- If any subagent fails, report the error
- Attempt to continue with available outputs
- Inform user which step failed and why
## Important Notes
- 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
This skill generates new content drafts by analyzing up to five reference pieces and running a structured pipeline that deconstructs, codifies, and re-creates content variations. It automates research, context gathering, meta-prompt creation, and produces three draft variations per content type with pre-flight checklists and source tracking. Outputs are saved with timestamped filenames for traceability.
You provide up to five reference URLs or paste content. The skill fetches and deconstructs those sources, builds a content anatomy guide, generates context questions, and crafts a meta-prompt that runs in two phases: an interview-style context gathering (up to 10 questions) and a content-writing phase that produces three variations per requested content type. All intermediate artifacts and final drafts are saved using a consistent timestamped naming convention.
How many reference URLs can I provide?
Up to five; at least one is required to start the pipeline.
What happens if a URL fails to fetch?
The system logs failures, continues with successful fetches, and reports the failed URLs and reasons.
How many variations are produced?
Exactly three variations per requested content type are generated in the content-writing phase.
Where are outputs saved and how are they named?
All artifacts are saved in designated folders with a single run timestamp in the format YYYY-MM-DD-HHmmss for traceability.