AI Content Generation

AI-powered content and image generation skill using the content-image-generation MCP server. This skill provides capabilities for generating marketing content with Claude Sonnet 4 or Gemini 2.0 Pro, creating images with Google Imagen 3/4, and generating videos with Google Veo 2/3.

Core Capabilities

Image Generation

• Generate images with Google Imagen 3 or Imagen 4
• Support multiple aspect ratios (1:1, 16:9, 4:3, 9:16, custom)
• Quality control (SD or HD)
• Batch generation for multiple images
• Prompt enhancement for better results
• Custom negative prompts for quality control
• Seed-based reproducibility

Content Generation

• Marketing content with Claude Sonnet 4 or Gemini 2.0 Pro
• Hero section copy and landing page content
• Blog posts and articles
• Product descriptions and feature lists
• Email campaigns and ad copy
• SEO-optimized content
• Tone and style customization

Video Generation

• Short-form videos with Google Veo 2 or Veo 3
• Multiple aspect ratios and durations
• Prompt-based video creation
• Quality and format options

Cost Optimization

• Pre-generation cost estimation
• Batch optimization recommendations
• Quality vs cost tradeoffs
• Token usage tracking

Instructions

Setup and Validation

Before generating content, verify MCP integration:

# Validate MCP server configuration
bash scripts/validate-mcp-setup.sh

# Setup environment variables
bash scripts/setup-environment.sh

Image Generation Workflow

1. Enhance Prompt: Use prompt enhancement for better results

``bash bash scripts/enhance-image-prompt.sh "your basic prompt" ``

1. Estimate Cost: Calculate generation costs

``bash bash scripts/calculate-cost.sh --type image --quality HD --count 5 ``

1. Generate Image: Use MCP tool with enhanced parameters

- Read template: templates/typescript/image-generation.ts or templates/python/image-generation.py - Call MCP tool: generateimageimagen3 or batchgenerateimages - Save to assets directory

1. Validate Output: Check image quality and dimensions

``bash bash scripts/validate-output.sh --type image --path /path/to/image.png ``

Content Generation Workflow

1. Define Requirements: Gather content parameters

- Content type (hero, blog, product, email) - Topic and keywords - Tone and style (professional, casual, technical) - Target audience - Desired length

1. Estimate Cost: Calculate generation costs

``bash bash scripts/calculate-cost.sh --type content --model claude-sonnet-4 --length 1000 ``

1. Generate Content: Use MCP tool

- Read template: templates/typescript/content-generation.ts or templates/python/content-generation.py - Call MCP tool: generatemarketingcontent - Save to content directory

1. Review and Refine: Validate content quality

``bash bash scripts/validate-output.sh --type content --path /path/to/content.md ``

Video Generation Workflow

1. Prepare Prompt: Create detailed video description

``bash bash scripts/enhance-video-prompt.sh "your video description" ``

1. Estimate Cost: Calculate video generation costs

``bash bash scripts/calculate-cost.sh --type video --duration 5 --quality HD ``

1. Generate Video: Use MCP tool

- Read template: templates/typescript/video-generation.ts or templates/python/video-generation.py - Call MCP tool: generatevideoveo3 - Save to assets directory

Batch Operations

For multiple assets, use batch generation:

# Optimize batch parameters
bash scripts/optimize-batch.sh --type image --count 10 --budget 50

# Generate batch with optimized settings
# Use batch_generate_images MCP tool

MCP Tools Reference

Image Generation

• generateimageimagen3: Generate single image with Imagen 3/4

- Parameters: prompt, negativeprompt, aspectratio, quality, model, seed - Returns: Base64 image data, metadata, cost

• batchgenerateimages: Generate multiple images efficiently

- Parameters: prompts array, shared settings, batch_size - Returns: Array of images with metadata

Content Generation

• generatemarketingcontent: Generate marketing copy

- Parameters: topic, content_type, tone, style, length, model, keywords - Returns: Generated content, metadata, cost

Video Generation

• generatevideoveo3: Generate video with Veo 2/3

- Parameters: prompt, duration, aspect_ratio, quality, model - Returns: Video data, metadata, cost

Utilities

• calculatecostestimate: Estimate generation costs

- Parameters: operation_type, parameters, quantity - Returns: Cost breakdown, recommendations

• imagepromptenhancer: Enhance image prompts

- Parameters: basicprompt, style, qualitylevel - Returns: Enhanced prompt, suggestions

Scripts Reference

All scripts are located in skills/ai-content-generation/scripts/:

• setup-environment.sh: Configure environment variables and credentials
• validate-mcp-setup.sh: Verify MCP server connection and tools
• enhance-image-prompt.sh: Improve image generation prompts
• calculate-cost.sh: Estimate generation costs before execution
• validate-output.sh: Check quality of generated assets
• optimize-batch.sh: Optimize batch generation parameters
• test-generation.sh: Run test generation to verify setup

Templates Reference

TypeScript Templates (templates/typescript/)

• image-generation.ts: Complete image generation implementation
• content-generation.ts: Marketing content generation
• video-generation.ts: Video generation workflow

Python Templates (templates/python/)

• image-generation.py: Complete image generation implementation
• content-generation.py: Marketing content generation
• video-generation.py: Video generation workflow

Examples

See examples/ directory for comprehensive usage examples:

• basic-usage.md: Simple image and content generation
• advanced-usage.md: Batch operations, cost optimization, custom parameters
• common-patterns.md: Hero images, blog headers, product galleries
• error-handling.md: Retry logic, fallbacks, validation
• integration.md: Astro integration, asset management, workflow automation

Best Practices

Image Generation

• Always enhance prompts for better quality
• Use HD quality for hero sections and key visuals
• Use SD quality for thumbnails and secondary images
• Specify negative prompts to avoid unwanted elements
• Use consistent seeds for reproducible results
• Batch similar images to optimize costs

Content Generation

• Provide clear topic and keywords
• Specify target audience for better relevance
• Choose appropriate tone and style
• Review and customize generated content
• Use Claude for technical/detailed content
• Use Gemini for creative/marketing content

Cost Optimization

• Estimate costs before generation
• Use batch operations for multiple assets
• Choose SD quality when HD is not required
• Optimize prompt length for content generation
• Cache and reuse similar assets
• Monitor token usage and costs

Error Handling

• Validate MCP setup before operations
• Check API quotas and limits
• Implement retry logic for transient failures
• Validate output quality after generation
• Log costs and metadata for tracking

Requirements

Environment Variables

• GOOGLECLOUDPROJECT: Google Cloud project ID for Vertex AI
• ANTHROPICAPIKEY: API key for Claude Sonnet content generation
• GOOGLEAIAPI_KEY: (Optional) API key for Gemini content generation

MCP Configuration

• content-image-generation MCP server must be configured in .mcp.json
• Google Cloud credentials must be set up for Vertex AI
• Appropriate APIs must be enabled (Vertex AI, Imagen, Veo)

Project Structure

• Assets directory for storing generated images/videos
• Content directory for storing generated text
• Environment file for API credentials

Skill Version: 1.0.0 Plugin: website-builder MCP Server: content-image-generation