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무료Generates paid ad copy for Google, Meta, and LinkedIn. Produces platform-ready variants using a creative matrix approach with headlines, descriptions, and image briefs.
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Generates marketing images using the mcp-image MCP server. Reads brand guidelines for style consistency. Supports social posts, blog headers, ads, and presentation visuals.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill generates images for marketing assets using the mcp__mcp-image__generate_image MCP tool. It reads brand guidelines to maintain visual consistency and constructs optimized prompts for each use case.
Required: The mcp-image MCP server must be configured and running.
Before generating any images, read docs/inputs/brand_guidelines.md.
If it exists and is filled in: Extract visual style, color palette, mood, motifs, composition preferences, and restrictions. Use these to constrain every prompt.
If it exists but is still a template (placeholder values): Tell the user:
"Your brand guidelines at
docs/inputs/brand_guidelines.mdare still the default template. I can generate images without them, but results will be generic. Would you like to fill in the guidelines first?"
If the user wants to proceed without guidelines, use sensible defaults: clean, minimal, professional, tech-focused.
Ask the user:
What is this image for?
Source content — What should the image represent?
Reference image — (Optional) Do you have a reference image for style matching?
Wait for answers before proceeding.
Based on the use case, select the appropriate aspect ratio and resolution:
| Use Case | Aspect Ratio | Resolution | Notes |
|---|---|---|---|
| LinkedIn post | 16:9 | 2K | Horizontal, feed-optimized |
| Twitter post | 16:9 | 2K | Horizontal, timeline-optimized |
| Blog featured image | 16:9 | 2K | Standard blog header |
| LinkedIn ad | 1:1 | 2K | Square format for sponsored content |
| Meta ad | 4:5 | 2K | Vertical, mobile feed-optimized |
| Presentation slide | 16:9 | 2K | Horizontal, projector-friendly |
| Instagram post | 1:1 | 2K | Square format |
Confirm the format with the user before generating. If the user requests a custom aspect ratio, use the closest supported option from the tool (1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9).
Build the prompt in three layers:
Extract from docs/inputs/brand_guidelines.md:
Derive the visual concept from the source content. Rules:
Always include:
[Style: visual style, color palette, mood from brand guidelines]
[Concept: single visual idea derived from source content]
[Composition: layout guidance based on use case]
No text, no words, no letters, no labels, no typography. No photorealistic human faces. Clean composition with clear focal point. [Additional brand restrictions]
Prompt rules:
Call the MCP tool with the constructed parameters:
mcp__mcp-image__generate_image(
prompt: [constructed prompt],
aspectRatio: [from format table],
imageSize: [from format table],
purpose: [use case description],
quality: "quality",
fileName: [descriptive file name]
)
File naming: [context]-[concept]-[use-case] (e.g., beyond-identity-agent-governance-linkedin, ceros-credential-flow-blog-header)
If a reference image was provided: Include inputImagePath with the absolute path.
After generation, read the output image and present it to the user.
After presenting the image, ask:
"Want to iterate on this, or is it good?"
If the user wants changes, adjust the prompt based on their feedback. Common adjustments:
| Feedback | Prompt Adjustment |
|---|---|
| "Too busy / complicated" | Reduce elements, add "minimal, sparse, lots of negative space" |
| "Too dark" | Shift to "light background, bright, well-lit" |
| "Too generic" | Add more specific visual motifs from the source content |
| "Wrong colors" | Explicitly name the desired colors |
| "Too many words/labels" | Strengthen the no-text restriction: "absolutely no text, no labels, no annotations, no typography of any kind" |
| "Not abstract enough" | Remove concrete objects, add "abstract, geometric, flowing shapes" |
Maximum 5 refinement rounds. After 5 attempts:
"We've done 5 rounds. To get closer to what you want, I'd recommend:"
- Providing a reference image that captures the style you're after
- Updating brand guidelines with more specific visual direction
- Trying a completely different visual concept
Images are saved to output/images/ automatically by the MCP tool.
After the image is approved, provide:
Image: output/images/[filename].jpg
Dimensions: [aspect ratio] at [resolution]
Prompt: [the prompt used, for future reference]
Alt text: [SEO-friendly description, under 125 characters]
When generating images for a set of assets (e.g., a blog post + social posts promoting it), maintain visual consistency:
/social-posts, /blog, or /ads, extract the concept directly from the generated content without re-asking the user.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Generates paid ad copy for Google, Meta, and LinkedIn. Produces platform-ready variants using a creative matrix approach with headlines, descriptions, and image briefs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want paid ad copy for Google, Meta, or LinkedIn, including headlines, descriptions, and image briefs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want campaign performance reviewed against benchmarks, with a health score and prioritized optimization recommendations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want a strict review of a marketing asset against style rules, factual accuracy, and messaging alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want to benchmark and improve an existing repo skill through repeated evals, prompt mutations, and score tracking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.