Content Generation Workflow

This skill provides a structured workflow for generating AI-powered content at scale using the repository's agent_codex.py automation tool. It handles the complete content generation pipeline from prompt creation to validated output.

When to Use This Skill

Use this skill when you need to:

• Generate multiple content pieces from templates
• Automate copywriting tasks for marketing materials
• Batch process content through AI models
• Transform existing content to match brand voice
• Create product descriptions or landing page copy at scale

Prerequisites

• Python 3.10 or higher installed
• OpenAI API key set in environment (for production runs)
• Git repository in clean state
• Input content prepared in appropriate directory

Workflow Overview

1. Prepare Input → 2. Create Prompt → 3. Test (Echo) → 4. Run (OpenAI) → 
5. Review Output → 6. Validate → 7. Move to Final → 8. Commit

Quick Start Example

# Test workflow
./scripts/agent_codex.py \
  --prompt ./prompts/your_prompt.txt \
  --input ./copywriting/source \
  --output ./docs/ai_outputs/test \
  --provider echo \
  --dry-run

# Production run
./scripts/agent_codex.py \
  --prompt ./prompts/your_prompt.txt \
  --input ./copywriting/source \
  --output ./docs/ai_outputs/prod \
  --provider openai \
  --model gpt-4o-mini

Common Use Cases

Generate Product Descriptions

./scripts/agent_codex.py \
  --prompt ./prompts/generate_product_descriptions.txt \
  --input ./copywriting/product_specs \
  --output ./docs/ai_outputs/product_descriptions \
  --provider openai

Rewrite to Brand Voice

./scripts/agent_codex.py \
  --prompt ./prompts/rewrite_to_insightful_voice.txt \
  --input ./copywriting/drafts \
  --output ./docs/ai_outputs/branded \
  --provider openai

Generate Landing Page Components

./scripts/agent_codex.py \
  --prompt ./prompts/generate_landing_components.txt \
  --input ./copywriting/component_outlines \
  --output ./docs/ai_outputs/components \
  --site ./landing_pages \
  --provider openai \
  --include-ext ".html,.css"

Configuration Reference

Provider Options

• openai: OpenAI API (requires OPENAI_API_KEY env var)
• echo: Test mode (no API calls)

Model Options

• gpt-4o-mini: Cost-effective (recommended)
• gpt-4o: Higher quality
• gpt-4: Premium quality

File Options

--include-ext ".md,.txt,.html,.css,.json"
--exclude-dirs ".git,node_modules,dist,build,docs/ai_outputs"

Best Practices

1. Always test first with --provider echo --dry-run
2. Review outputs before moving to production locations
3. Use specific prompts with clear instructions and constraints
4. Monitor costs by tracking API usage
5. Commit separately generated vs manually edited content

Troubleshooting

No files processed: Check file extensions and directory paths

API rate limits: Reduce batch size or add delays

Poor quality: Refine prompt with more examples and constraints

HTML errors: Add HTML template to prompt or post-process outputs

Resources

• Script: scripts/agent_codex.py
• Prompts: prompts/
• Outputs: docs/ai_outputs/
• Help: ./scripts/agent_codex.py --help