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 prime-skills/runcomfy-agent-skills --skill image-editgit clone --depth 1 https://github.com/prime-skills/runcomfy-agent-skillsWrote 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/prime-skills/runcomfy-agent-skills/image-edit)<a href="https://agentmods.dev/skills/prime-skills/runcomfy-agent-skills/image-edit"><img src="https://agentmods.dev/badge/skills/prime-skills/runcomfy-agent-skills/image-edit/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/prime-skills/runcomfy-agent-skills/image-edit"><img src="https://agentmods.dev/badge/skills/prime-skills/runcomfy-agent-skills/image-edit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00186 | $0.03481 |
| Opus 5.5 | $0.00074 | $0.01392 |
| Sonnet 5.5 | $0.00037 | $0.00696 |
| Haiku 4.5 | $0.00019 | $0.00348 |
Grade A, and why
image-edit 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- image-edit — 100% identical, 0 lines differ
- image-edit — 100% identical, 0 lines differ
- image-edit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Edit — Pro Pack on RunComfy
runcomfy.com · Nano Banana Edit · GPT Image 2 Edit · Flux Kontext · Z-Image Inpaint · GitHub
Image edit, intent-routed. This skill doesn't lock you to one model — it picks the right edit model in the RunComfy catalog based on what the user actually wants: batch identity-preservation, multilingual text rewrite, single-shot precise edit, or mask-driven region replacement.
npx skills add agentspace-so/runcomfy-skills --skill image-edit -g
Pick the right model for the user's intent
| User intent | Model | Why |
|---|---|---|
| Batch edit 1–20 images consistently (SKU gallery, A/B variants) | Nano Banana Edit | Up to 20 input images per call; locked aspect/resolution for series |
| Swap background, preserve subject identity | Nano Banana Edit | Strong identity preservation under "keep X unchanged" prompts |
| Localized object removal / addition with spatial language ("the left object", "upper-right corner") | Nano Banana Edit | Honors directional spatial scope |
| Multilingual / non-Latin in-image text rewrite (Japanese kana, Cyrillic, Arabic) | GPT Image 2 Edit | Strongest in class for multilingual typography |
| Multi-reference composition (subject from img1, scene from img2, palette from img3) | GPT Image 2 Edit | Numbered refs route cues correctly |
| Layout-precise repositioning ("move headline from top-right to bottom-center") | GPT Image 2 Edit | Directional language honored at layout level |
| Identity preservation across translated headline variants | GPT Image 2 Edit | Same source asset → many language variants, identity stable |
| Single-shot precise local edit ("she's now holding an orange umbrella") | Flux Kontext Pro | Single-ref single-instruction, high-fidelity preservation |
| Mask-driven object removal (cables, watermarks, distractions) | Z-Image Turbo Inpaint | Mask-required, strength-tunable, edge-consistent |
| Mask-driven region replacement (full background swap with mask) | Z-Image Turbo Inpaint | High strength + clean mask = clean replacement |
| Default if unspecified | Nano Banana Edit | Most flexible, supports both single and batch |
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 · 271 lines · 186 tokens per session scan A 17c1b6b5c8c8
image-edit is a skill published in the GitHub repository prime-skills/runcomfy-agent-skills (60 stars, last pushed 5mo ago), licensed MIT. It adds 186 tokens to every session and 3,481 once invoked, about $0.0007 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 2026-08-30.
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