content-draft-generator
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.
Best use case
content-draft-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using content-draft-generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/content-draft-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How content-draft-generator Compares
| Feature / Agent | content-draft-generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Content Draft Generator
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
3. Save to `content-anatomy/anatomy-{timestamp}.md`
4. 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
3. Save to `content-context/context-{timestamp}.md`
4. 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
2. Save to `content-meta-prompt/meta-prompt-{timestamp}.md`
3. 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
2. 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
3. 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 2Related Skills
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