Skill Compose is an open-source platform for building and running AI agents that use modular skills. It is intended for creating skill-powered agents without workflow graphs or a command-line interface, and the catalogue skills are examples of those agent capabilities.
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 dp-archive/archive --skill imagegengit clone --depth 1 https://github.com/dp-archive/archiveWrote 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/dp-archive/archive/imagegen)<a href="https://agentmods.dev/skills/dp-archive/archive/imagegen"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/imagegen/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/dp-archive/archive/imagegen"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/imagegen.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 | $0.00074 | $0.02206 |
| Opus 5.5 | $0.00030 | $0.00882 |
| Sonnet 5.5 | $0.00015 | $0.00441 |
| Haiku 4.5 | $0.00007 | $0.00221 |
Grade A, and why
imagegen 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.
This is a copy
100% identical to imagegen — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Skill
Generates or edits images for the current project (e.g., website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, infographics). Defaults to gpt-image-1.5 and the OpenAI Image API, and prefers the bundled CLI for deterministic, reproducible runs.
When to use
- Generate a new image (concept art, product shot, cover, website hero)
- Edit an existing image (inpainting, masked edits, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
- Batch runs (many prompts, or many variants across prompts)
Decision tree (generate vs edit vs batch)
- If the user provides an input image (or says “edit/retouch/inpaint/mask/translate/localize/change only X”) → edit
- Else if the user needs many different prompts/assets → generate-batch
- Else → generate
Workflow
- Decide intent: generate vs edit vs batch (see decision tree above).
- Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input image(s)/mask(s). For multi-image edits, label each input by index and role; for edits, list invariants explicitly.
- If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
- Augment prompt into a short labeled spec (structure + constraints) without inventing new creative requirements.
- Run the bundled CLI (
scripts/image_gen.py) with sensible defaults (see references/cli.md). - For complex edits/generations, inspect outputs (open/view images) and validate: subject, style, composition, text accuracy, and invariants/avoid items.
- Iterate: make a single targeted change (prompt or mask), re-run, re-check.
- Save/return final outputs and note the final prompt + flags used.
Temp and output conventions
- Use
tmp/imagegen/for intermediate files (for example JSONL batches); delete when done. - Write final artifacts under
output/imagegen/when working in this repo. - Use
--outor--out-dirto control output paths; keep filenames stable and descriptive.
What ships with it
10 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 · 175 lines · 74 tokens per session scan A 4da06e7a1fbf
imagegen is a skill published in the GitHub repository dp-archive/archive (1,105 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 2,206 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to imagegen, differing in 0 lines, and is treated as a copy.
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