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Content Draft Generator

  • 14 installs
  • 638 repo stars
  • Updated March 7, 2026
  • sundial-org/awesome-openclaw-skills

Helps with ai & agent building tasks during AI-assisted development.

About

content-draft-generator is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • content-draft-generator
  • AI & Agent Building
  • AI-coding skill

Content Draft Generator by the numbers

  • 14 all-time installs (skills.sh)
  • Ranked #11,275 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/sundial-org/awesome-openclaw-skills --skill content-draft-generator

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Listed on Skillselion
Installs14
repo stars638
Last updatedMarch 7, 2026
Repositorysundial-org/awesome-openclaw-skills

What it does

Helps with ai & agent building tasks during AI-assisted development.

Files

SKILL.mdMarkdownGitHub ↗

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 2

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