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.
git clone --depth 1 https://github.com/Comfy-Org/comfy-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/commands/comfy-org/comfy-skills/combine-people)<a href="https://agentmods.dev/commands/comfy-org/comfy-skills/combine-people"><img src="https://agentmods.dev/badge/commands/comfy-org/comfy-skills/combine-people/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/commands/comfy-org/comfy-skills/combine-people"><img src="https://agentmods.dev/badge/commands/comfy-org/comfy-skills/combine-people.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 | $0.00010 | $0.00889 |
| Opus 5 | $0.00005 | $0.00445 |
| Sonnet 5 | $0.00002 | $0.00178 |
| Haiku 4.5 | $0.00001 | $0.00089 |
Grade A, and why
combine-people 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 12d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Combine a user's photo with another person (real or fictional) into a single composite image: $ARGUMENTS
Follow these steps exactly:
Step 1: Upload the user's photo
If the user provides a photo of themselves, use upload_file to upload it. Note the returned filename for use in LoadImage.
Step 2: Generate a reference image of the other person
Use KlingOmniProImageNode (model: kling-v3-omni) or GeminiNanoBanana2 to generate a high-quality reference portrait of the target person. Use a detailed prompt describing their iconic appearance. Save and upload the result for use as a second reference.
Alternatively, if the user provides their own reference image of the second person, upload that instead and skip generation.
Step 3: Batch the reference images
Use ImageBatch to combine both images into a single batch:
{
"class_type": "ImageBatch",
"inputs": { "image1": ["user_photo_node", 0], "image2": ["other_person_node", 0] }
}
Step 4: Generate the composite with Nano Banana 2
Use GeminiNanoBanana2 with these settings for best results:
- model:
Nano Banana 2 (Gemini 3.1 Flash Image) - thinking_level:
HIGH(critical for face accuracy) - resolution:
2K - aspect_ratio:
16:9(or match user preference) - response_modalities:
IMAGE - images: connect to the ImageBatch output
Prompt structure (important):
The prompt should:
- Explicitly state which reference image is which ("The first image is...", "The second image is...")
- Emphasize EXACT face reproduction from both references
- Describe clothing, pose, and setting
- Specify lighting consistency
- Call out any artifacts to avoid (e.g., "NO glare on glasses, NO reflections on lenses")
Example prompt:
Create a black and white vintage 1970s photograph combining these two people.
The first image is the primary reference - reproduce this man's face EXACTLY as shown:
his specific facial features, smile, dark hair style, and black zip-up jacket. He should
be on the left. The second image shows [PERSON NAME] - reproduce their face exactly as
shown with [iconic features]. They are posing together as close friends in [setting].
[Style instructions]. Important: [any artifact avoidance instructions].
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.
- 12d ago First seen · 88 lines · 10 tokens per session scan A 3244d9f2d34a
combine-people is a command published in the GitHub repository Comfy-Org/comfy-skills (193 stars, last pushed 2d ago), licensed MIT. It adds 10 tokens to every session and 889 once invoked, about $0.0001 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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