image-workflow
Structured workflow for generating, evaluating, and storing images via Gemini Imagen API
Skill by minsley
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/minsley-image-workflow
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: backend, productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/image-workflow
What's inside
- Skill name
image-workflow- Description
- Structured workflow for generating, evaluating, and storing images via Gemini Imagen API
- License
- MIT
- Instructions
- 69 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Image Workflow Use this skill when you've been assigned an image generation task. Follow this structured process to produce high-quality results. ## 1. Read the Brief 1. Pull the vault: `cd /opt/vault && git pull origin main --rebase` 2. Read the task file at `010-tasks/active/<task-id>.md` 3. Extract: - **Subject**: What to generate - **Style**: Art style, mood, aesthetic - **Dimensions**: Aspect ratio requirements - **Quantity**: How many final images - **Criteria**: Quality standards, must-haves, must-avoids ## 2. Craft Initial Prompts Write 2-3 prompt variants that approach the subject differently: - Vary style keywords (photorealistic vs. illustrated vs. abstract) - Vary composition (close-up vs. wide shot vs. bird's eye) - Keep core subject consistent across variants ## 3. Generate Initial Set Use `generate_image` to produce initial candidates: ``` generate_image(prompt="<detailed prompt>", aspect_ratio="1:1", num_images=2) ``` Generate from each prompt variant to build a diverse candidate pool. ## 4. Evaluate Against Criteria For each generated image, assess: - Does it match the requested subject? - Does the style match requirements? - Is the composition effective? - Are there artifacts or quality issues? - Does it meet the specific acceptance criteria from the task? Note: You cannot view the images directly. Evaluate based on the generation parameters and any metadata returned. If the task has specific visual criteria, note them for the task requester to verify. ## 5. Refine and Iterate Based on evaluation: - Adjust prompts to fix issues (e.g., add "no text" if unwanted text appears) - Try different aspect ratios if composition isn't working - Generate additional variants of the best-performing prompts - Aim for 2-3 rounds maximum to avoid excessive API usage ## 6. Save Final Results Save selected images to the vault: ``` save_generated_image(base64_png="<data>", output_path="/opt/vault/030-knowledge/assets/<task-id>/image-01.png") ``` Create a gallery page documenting each image with its generation prompt and parameters. ## 7. Submit Results Update the task file with: - `status: completed` - Result section with number of images generated, prompts used, and gallery link - Any notes about quality or limitations Create a vault PR with all generated assets and the gallery page.
From minsley/lobmob, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Feb 13, 2026 (8 months ago)
- Last release
- Feb 26, 2026 (8 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/minsley/lobmob
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
image-workflow skill. Agent Mart. https://agentmart.dev/skills/minsley-image-workflow (last updated 2026-10-24)