Content Draft Generator OpenClaw Skill - ClawHub
Do you want your AI agent to automate Content Draft Generator workflows? This free skill from ClawHub helps with marketing & sales tasks without building custom tools from scratch.
What this skill does
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.
Install
npx clawhub@latest install content-draft-generatorFull SKILL.md
Open original| name | version | description |
|---|---|---|
| content-draft-generator | 1.0.2 | 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. |
Content Draft Generator
π 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 contentreferences/content-anatomy-generator.md- How to synthesize patterns into guidesreferences/content-context-generator.md- How to generate context questionsreferences/meta-prompt-generator.md- How to create the final prompt
Workflow Overview
Step 1: Collect Reference URLs (up to 5)
Step 2: Content Deconstruction
β Fetch and analyze each URL
β Save to content-breakdown/breakdown-{timestamp}.md
Step 3: Content Anatomy Generation
β Synthesize patterns into comprehensive guide
β Save to content-anatomy/anatomy-{timestamp}.md
Step 4: Content Context Generation
β Generate context questions needed from user
β Save to content-context/context-{timestamp}.md
Step 5: Meta Prompt Generation
β Create the content generation prompt
β Save to content-meta-prompt/meta-prompt-{timestamp}.md
Step 6: Execute Meta Prompt
β Phase 1: Context gathering interview (up to 10 questions)
β Phase 2: Generate 3 variations of each content type
Step 7: Save Content Drafts
β Save to content-draft/draft-{timestamp}.md
Step-by-Step Instructions
Step 1: Collect Reference URLs
- Ask the user: "Please provide up to 5 reference content URLs that exemplify the type of content you want to create."
- Accept URLs one by one or as a list
- Validate URLs before proceeding
- If user provides no URLs, ask them to provide at least 1
Step 2: Content Deconstruction
- Fetch content from all reference URLs (use web_fetch tool)
- For Twitter/X URLs, transform to FxTwitter API:
https://api.fxtwitter.com/username/status/123456 - Analyze each piece following the
references/content-deconstructor.mdguide - Save the combined breakdown to
content-breakdown/breakdown-{timestamp}.md - Report: "β Content breakdown saved"
Step 3: Content Anatomy Generation
- Using the breakdown from Step 2, synthesize patterns following
references/content-anatomy-generator.md - Create a comprehensive guide with:
- Core structure blueprint
- Psychological playbook
- Hook library
- Fill-in-the-blank templates
- Save to
content-anatomy/anatomy-{timestamp}.md - Report: "β Content anatomy guide saved"
Step 4: Content Context Generation
- Analyze the anatomy guide following
references/content-context-generator.md - Generate context questions covering:
- Topic & subject matter
- Target audience
- Goals & outcomes
- Voice & positioning
- Save to
content-context/context-{timestamp}.md - Report: "β Context requirements saved"
Step 5: Meta Prompt Generation
- 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
- Save to
content-meta-prompt/meta-prompt-{timestamp}.md - Report: "β Meta prompt saved"
Step 6: Execute Meta Prompt
-
Begin Phase 1: Context Gathering
- Interview the user with questions from context requirements
- Ask up to 10 questions
- Wait for user responses between questions
-
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
- Save complete output to
content-draft/draft-{timestamp}.md - Include:
- Context summary from Phase 1
- All 3 content variations with their hook approaches
- Pre-flight checklists for each variation
- Report: "β Content drafts saved"
File Naming Convention
All generated files use timestamps: {type}-{YYYY-MM-DD-HHmmss}.md
Examples:
breakdown-2026-01-20-143052.mdanatomy-2026-01-20-143125.mdcontext-2026-01-20-143200.mdmeta-prompt-2026-01-20-143245.mddraft-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 filesβnever overwrite previous runs
- Wait for user input during Phase 1 context gathering
- Generate exactly 3 variations in Phase 2