SEO Image Gen

SEO Image Gen: AI Image Generation for SEO Assets (Extension)

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/seo-image-gen from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

SEO Image Gen: AI Image Generation for SEO Assets (Extension) Generate production-ready images for SEO use cases using Gemini's image generation via the banana Creative Director pipeline. Maps SEO needs to optimized domain modes, aspect ratios, and resolution defaults.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Architecture Note, Prerequisites, SEO Image Use Cases, Generation Pipeline, Post-Generation SEO Checklist, Cost Awareness.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when generating OG images, hero images, schema visuals, infographics, or similar SEO assets.
• Use when image generation is part of a broader SEO or publishing workflow.
• Use only when the required image-generation extension is available.
• Use when the request clearly matches the imported source intent: Generate SEO-focused images such as OG cards, hero images, schema assets, product visuals, and infographics. Use when image generation is part of an SEO workflow or content publishing task.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Architecture Note

This skill has two components with distinct roles:

• SKILL.md (this file): Handles interactive /seo image-gen commands for generating images
• Agent (agents/seo-image-gen.md): Audit-only analyst spawned during /seo audit to assess existing OG/social images and produce a generation plan (never auto-generates)

Examples

Example 1: Ask for the upstream workflow directly

Use @seo-image-gen to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @seo-image-gen against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @seo-image-gen for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @seo-image-gen using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/seo-image-gen, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Quick Reference

Imported: Reference Documentation

Load on-demand. Do NOT load all at startup:

• references/prompt-engineering.md:6-component system, domain modes, templates
• references/gemini-models.md:Model specs, rate limits, capabilities
• references/mcp-tools.md:MCP tool parameters and responses
• references/post-processing.md:ImageMagick/FFmpeg pipeline recipes
• references/cost-tracking.md:Pricing, usage tracking
• references/presets.md:Brand preset management
• references/seo-image-presets.md:SEO-specific preset templates

Imported: Prerequisites

This skill requires the banana extension to be installed:

./extensions/banana/install.sh

Check availability: Before using any image generation tool, verify the MCP server is connected by checking if gemini_generate_image or set_aspect_ratio tools are available. If tools are not available, inform the user the extension is not installed and provide install instructions.

Imported: SEO Image Use Cases

Each use case maps to pre-configured banana parameters:

Imported: Generation Pipeline

For every generation request:

1. Identify use case from command or context (og, hero, product, etc.)
2. Apply SEO defaults from the use cases table above
3. Set aspect ratio via set_aspect_ratio MCP tool
4. Construct Reasoning Brief using the banana Creative Director pipeline:
   • Load references/prompt-engineering.md for the 6-component system
   • Apply domain mode emphasis (Subject 30%, Style 25%, Context 15%, etc.)
   • Be SPECIFIC and VISCERAL: describe what the camera sees
5. Generate via gemini_generate_image MCP tool
6. Post-generation SEO checklist (see below)

Check for Presets

If the user mentions a brand or has SEO presets configured:

python3 ~/.claude/skills/seo-image-gen/scripts/presets.py list

Load matching preset and apply as defaults. Also check references/seo-image-presets.md for SEO-specific preset templates.

Imported: Post-Generation SEO Checklist

After every successful generation, guide the user on:

1. Alt text:Write descriptive, keyword-rich alt text for the generated image
2. File naming:Rename to SEO-friendly format: keyword-description-widthxheight.webp
3. WebP conversion:Convert to WebP for optimal page speed:

   magick output.png -quality 85 output.webp
   

4. File size:Target under 200KB for hero images, under 100KB for thumbnails
5. Schema markup:Suggest ImageObject schema for the generated image:

   {
     "@type": "ImageObject",
     "url": "https://example.com/images/keyword-description.webp",
     "width": 1200,
     "height": 630,
     "caption": "Descriptive caption with target keyword"
   }
   

6. OG meta tags:For social preview images, remind about:

   <meta property="og:image" content="https://example.com/images/og-image.webp" />
   <meta property="og:image:width" content="1200" />
   <meta property="og:image:height" content="630" />
   <meta property="og:image:alt" content="Descriptive alt text" />
   

Imported: Cost Awareness

Image generation costs money. Be transparent:

• Show estimated cost before generating (especially for batch)
• Log every generation: python3 ~/.claude/skills/seo-image-gen/scripts/cost_tracker.py log --model MODEL --resolution RES --prompt "brief"
• Run cost_tracker.py summary if user asks about usage

Approximate costs (gemini-3.1-flash):

• 512: ~$0.02/image
• 1K resolution: ~$0.04/image
• 2K resolution: ~$0.08/image
• 4K resolution: ~$0.16/image

Imported: Model Routing

Imported: Error Handling

Imported: Cross-Skill Integration

• seo-images (analysis) feeds into seo-image-gen (generation): audit results from /seo images identify missing or low-quality images; use those findings to drive /seo image-gen commands
• seo-audit spawns the seo-image-gen agent (not this skill) to analyze OG/social images across the site and produce a prioritized generation plan
• seo-schema can consume generated images: after generation, suggest ImageObject schema markup pointing to the new assets

Imported: Response Format

After generating, always provide:

1. Image path:where it was saved
2. Crafted prompt:show what was sent to the API (educational)
3. Settings:model, aspect ratio, resolution
4. SEO checklist:alt text suggestion, file naming, WebP conversion
5. Schema snippet:ImageObject or og:image markup if applicable

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.