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 j4flmao/agent-skills --skill genai-visiongit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/genai-vision)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/genai-vision"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/genai-vision/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/j4flmao/agent-skills/genai-vision"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/genai-vision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00143 | $0.02015 |
| Opus 5.5 | $0.00057 | $0.00806 |
| Sonnet 5.5 | $0.00029 | $0.00403 |
| Haiku 4.5 | $0.00014 | $0.00201 |
Grade A, and why
genai-vision 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 yesterday.
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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GenAI Vision & Audio
Purpose
Ship image generation and speech transcription with open models: pick a model whose license fits the use case, load it in half precision with sensible memory settings, make outputs reproducible, and wrap it in a service that handles queuing and safety. The diffusers library provides the image pipelines; Whisper (via faster-whisper or transformers) handles audio.
Agent Protocol
Trigger
"stable diffusion", "SDXL", "FLUX", "diffusers", "text-to-image", "img2img", "inpainting", "LoRA for SD", "Whisper", "transcribe audio".
Input Context
- Use case (commercial or not), target quality, latency per image.
- GPU (VRAM size) or CPU-only constraints.
- Need for conditioning (img2img, inpainting, ControlNet, LoRA styles).
- Audio language(s) and real-time vs batch transcription.
Output Artifact
Model choice with license note, pipeline code, memory/performance settings, serving outline.
Response Format
Model: {repo id} License: {license} VRAM: {approx}
Pipeline: {class} dtype: {fp16|bf16} Steps/guidance: {n}/{g}
Memory: {cpu offload|vae tiling|none}
Repro: seed={n} recorded with prompt + model revision
Audio: {whisper variant, compute_type}
Completion Criteria
- License verified for intended use.
- Device selected dynamically; half precision used on GPU.
- Seed, model revision, and parameters logged per output.
- Safety/NSFW policy defined for user-facing generation.
Model Selection (verify licenses on the model card before use)
| Model family | Notes | License (check card) |
|---|---|---|
| Stable Diffusion 1.5 | Small, huge LoRA ecosystem; original runwayml repo was removed, mirrors exist | CreativeML OpenRAIL-M |
| SDXL base 1.0 | 1024px, good quality/VRAM balance | CreativeML OpenRAIL++-M |
| SD 3.5 (Medium/Large) | MMDiT architecture, better text rendering | Stability AI Community License (revenue threshold) |
| FLUX.1 [schnell] | Few-step, high quality | Apache-2.0 |
| FLUX.1 [dev] | Higher quality, guidance-distilled | Non-commercial license |
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.
- yesterday Changed · +136 lines · +120 tokens per session e0c3155f9599
- 1mo ago First seen · 36 lines · 23 tokens per session scan A 4f86aedda75d
genai-vision is a skill published in the GitHub repository j4flmao/agent-skills (35 stars, last pushed yesterday), licensed MIT. It adds 143 tokens to every session and 2,015 once invoked, about $0.0006 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.
Other skills, from other repositories
multimodal-stable-diffusion
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
diffusers-ascend-pipeline
A guide for running image and video generation pipelines on Huawei Ascend NPUs with the Diffusers library. Diffusers is a software library for using generative models, and the guide covers model pipelines, memory settings, LoRA adapters, and multi-card inference.
stable-diffusion-image-generation
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
stable-diffusion-image-generation
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
stable-diffusion-image-generation
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
stable-diffusion-image-generation
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.