Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/shinpr/mcp-image/image-generationnpx skills add shinpr/mcp-image --skill image-generationgit clone --depth 1 https://github.com/shinpr/mcp-imageWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shinpr/mcp-image/image-generation)<a href="https://agentmods.dev/skills/shinpr/mcp-image/image-generation"><img src="https://agentmods.dev/badge/skills/shinpr/mcp-image/image-generation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01212 |
| Opus 5 | $0.00022 | $0.00606 |
| Sonnet 5 | $0.00009 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
image-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Prompt Best Practices
Prompt Structure
Enhance every image generation prompt around three core elements:
1. SUBJECT (What)
- Physical characteristics: textures, materials, colors, scale
- Actions, poses, expressions if applicable
- Distinctive features that define the subject
2. CONTEXT (Where/When)
- Setting, background, spatial relationships (foreground, midground, background)
- Time of day, weather, atmospheric conditions
- Mood and emotional tone of the scene
3. STYLE (How)
- Artistic or photographic approach: reference specific artists, movements, or styles
- Camera/lens choices: specify focal length, aperture, and shooting angle when photographic
Core Principles
- Preserve intent — Add visual details (lighting, texture, composition) only in areas the user left unspecified; keep all user-specified elements unchanged
- Positive descriptions only — Describe what should be present; rephrase any exclusion as an inclusion
- Specific over vague — "golden hour sunlight at 15° angle" beats "nice lighting"
- Natural flow — Weave elements into a single flowing description, not a bullet list
Output Format
Return the enhanced prompt as a single flowing paragraph. When the user provides multiple requests, return each as a separate enhanced prompt under a labeled heading.
Enhancement Patterns
Hyper-Specific Details
Add concrete visual details for any Subject/Context/Style element not specified by the user:
- Lighting → direction, quality, color temperature, shadow behavior
- Textures → surface materials, weathering, reflectivity
- Atmosphere → particulates, humidity, depth haze
- Scale → relative sizes, distances, proportions
Camera Control Terminology
When a photographic look is appropriate:
- Lens type: "shot with 85mm portrait lens", "wide-angle 24mm"
- Aperture: "shallow depth of field at f/1.8", "deep focus at f/11"
- Angle: "low angle emphasizing height", "bird's eye view"
- Motion: "motion blur on the paws", "frozen mid-action"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 138 lines · 43 tokens per session scan A 43acbbc62107
image-generation is a skill published in the GitHub repository shinpr/mcp-image (157 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 1,212 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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