Generate high-quality images via Gemini models with structured prompts, aspect ratios, and brand validation.
Works with
Supports multiple Gemini models (gemini-3-pro-image, gemini-2.5-flash-image, gemini-2.5-pro-image) optimized for different quality and speed tradeoffs
Enforces structured prompt format covering subject, style, lighting, mood, composition, aspect ratio, and brand colors to ensure consistent outputs
Includes validation workflows across multiple agents for prompt completeness, st
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimage-generation-mcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches image-generation-mcp from supercent-io/skills-template and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate image-generation-mcp. Access via /image-generation-mcp in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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MCP를 통한 AI 이미지 생성 스킬입니다. Gemini 모델 또는 호환 서비스를 사용하여 마케팅, UI, 프레젠테이션용 고품질 이미지를 생성합니다.
# MCP 서버 설정 확인
claude mcp list
# Gemini CLI 사용 가능 여부 확인
# gemini-cli가 설치되어 있어야 함
필수 설정:
구조화된 프롬프트 작성:
**Subject**: [주요 피사체]
**Style**: [스타일 - 미니멀, 일러스트, 사진풍, 3D 등]
**Lighting**: [조명 - 자연광, 스튜디오, 골든아워 등]
**Mood**: [분위기 - 차분한, 역동적, 전문적 등]
**Composition**: [구성 - 중앙 배치, 삼분할 등]
**Aspect Ratio**: [비율 - 16:9, 1:1, 9:16]
**Brand Colors**: [브랜드 컬러 제약사항]
| 모델 | 용도 | 특징 |
|---|---|---|
gemini-3-pro-image |
고품질 | 복잡한 구성, 디테일 |
gemini-2.5-flash-image |
빠른 반복 | 프로토타이핑, 테스트 |
gemini-2.5-pro-image |
균형 | 품질/속도 밸런스 |
# 2-4개 변형 생성
ask-gemini "Create a serene mountain landscape at sunset,
wide 16:9, minimal style, soft gradients in brand blue #2563EB"
# 단일 변수 변경으로 반복
ask-gemini "Same prompt but with warm orange tones"
리뷰 체크리스트:
최종 산출물:
{
"prompt": "serene mountain landscape at sunset...",
"model": "gemini-3-pro-image",
"aspect_ratio": "16:9",
"style": "minimal",
"brand_colors": ["#2563EB"],
"output_file": "hero-image-v1.png",
"timestamp": "2026-01-21T10:30:00Z"
}
Prompt:
Create a serene mountain landscape at sunset,
wide 16:9, minimal style, soft gradients in brand blue #2563EB.
Focus on clean lines and modern aesthetic.
Expected output:
Prompt:
Generate a 1:1 thumbnail of a futuristic dashboard UI
with clean interface, soft lighting, and professional feel.
Include subtle glow effects and dark theme.
Expected output:
Prompt:
Create a LinkedIn banner (1584x396) for a SaaS startup.
Modern gradient background with abstract geometric shapes.
Colors: #6366F1 to #8B5CF6.
Leave space for text overlay on the left side.
Expected output:
Cause: 안정적인 스타일 제약 누락 Solution: 스타일 레퍼런스와 고정 팔레트 추가
Cause: 비율 미지정 또는 지원하지 않는 비율 Solution: 정확한 비율 제공 후 재생성
Cause: 컬러 코드 미지정 Solution: HEX 코드로 브랜드 컬러 명시
## Image Generation Report
### Request
- **Prompt**: [full prompt]
- **Model**: [model used]
- **Ratio**: [aspect ratio]
### Output Files
1. `filename-v1.png` - [description]
2. `filename-v2.png` - [variant description]
### Metadata
- Generated: [timestamp]
- Iterations: [count]
- Selected: [final choice]
### Usage Notes
[Any notes for implementation]
| Agent | Role |
|---|---|
| Claude | 프롬프트 구성, 품질 검증 |
| Gemini | 이미지 생성 실행 |
| Codex | 파일 관리, 배치 처리 |
#image-generation #gemini #mcp #design #creative #ai-art
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
image-generation-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in image-generation-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: image-generation-mcp is focused, and the summary matches what you get after install.
We added image-generation-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for image-generation-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
image-generation-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: image-generation-mcp is focused, and the summary matches what you get after install.
Registry listing for image-generation-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
image-generation-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
image-generation-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
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