content-draft-generator

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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

mkdir -p .claude/skills/content-draft-generator && curl -L -o skill.zip "https://mcp.directory/api/skills/download/8475" && unzip -o skill.zip -d .claude/skills/content-draft-generator && rm skill.zip

Installs to .claude/skills/content-draft-generator

About this skill

Content Draft Generator

🔒 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 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
  1. Save to content-meta-prompt/meta-prompt-{timestamp}.md
  2. 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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