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 skills add comisai/comis --skill image-generationgit clone --depth 1 https://github.com/comisai/comisWrote 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/comisai/comis/image-generation)<a href="https://agentmods.dev/skills/comisai/comis/image-generation"><img src="https://agentmods.dev/badge/skills/comisai/comis/image-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/comisai/comis/image-generation"><img src="https://agentmods.dev/badge/skills/comisai/comis/image-generation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00072 | $0.01091 |
| Opus 5.5 | $0.00029 | $0.00436 |
| Sonnet 5.5 | $0.00014 | $0.00218 |
| Haiku 4.5 | $0.00007 | $0.00109 |
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 1mo 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Generate high-quality images using structured JSON prompts and a bundled Python script. Supports reference images for style and composition guidance.
All script paths below are relative to this skill's directory. Resolve them against the directory containing the manifest file shown in <location> (e.g., if <location> is ~/.comis/skills/image-generation/SKILL.md, then scripts/generate.py means ~/.comis/skills/image-generation/scripts/generate.py).
Invoke the resolved script by its absolute path while keeping the tool working directory inside the execution workspace. Never set cwd to the skill directory; it is outside workspace bounds. In command examples below, replace each relative scripts/... path with its resolved absolute path.
Write prompt files and generated outputs to your workspace directory (shown in the "Workspace" section of your system prompt).
The bundled script requires requests and Pillow Python packages (pip install requests Pillow).
Workflow
Step 1: Understand requirements
Identify from the user's request:
- Subject/content: What should be in the image
- Style preferences: Art style, mood, color palette
- Technical specs: Aspect ratio, composition, lighting
- Reference images: Any images to guide generation
Step 2: Create structured prompt
Write a JSON prompt file to your workspace directory with a descriptive filename like {subject-name}.json.
Step 3: Execute generation
python3 scripts/generate.py \
--prompt-file ~/.comis/workspace/prompt-file.json \
--output-file ~/.comis/workspace/generated-image.jpg \
--aspect-ratio 16:9
With reference images:
python3 scripts/generate.py \
--prompt-file ~/.comis/workspace/prompt-file.json \
--reference-images /path/to/ref1.jpg /path/to/ref2.png \
--output-file ~/.comis/workspace/generated-image.jpg \
--aspect-ratio 2:3
Parameters:
--prompt-file: Path to JSON prompt file (required)--reference-images: Paths to reference images (optional, space-separated)--output-file: Path to output image file (required)--aspect-ratio: Aspect ratio (optional, default: 16:9)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 123 lines · 72 tokens per session scan A 0ca8a0be9fba
image-generation is a skill published in the GitHub repository comisai/comis (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,091 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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