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 agentmods add skills/g1joshi/agent-skills/stable-diffusionnpx skills add G1Joshi/Agent-Skills --skill stable-diffusiongit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/stable-diffusion)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/stable-diffusion"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/stable-diffusion.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00303 |
| Opus 5 | $0.00009 | $0.00151 |
| Sonnet 5 | $0.00003 | $0.00061 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
stable-diffusion 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 4d 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.
What it actually says
Stable Diffusion
Stable Diffusion (by Stability AI) is the open standard for image generation. SD 3.5 (2025) improves prompt adherence and typography.
When to Use
- Control: You need exact composition control (ControlNet).
- Local Generation: Run on your own GPU. No censorship/cost.
- Fine-Tuning: training LoRAs on your own face/product.
Core Concepts
Diffusers Library
The Hugging Face library to run SD pipelines in Python.
LoRA (Low-Rank Adaptation)
Small adapter files (100MB) that add a style or character to the base model.
ComfyUI
The node-based GUI for building complex SD workflows (see separate skill).
Best Practices (2025)
Do:
- Use SD 3.5 Large: For best text rendering.
- Use Flux: The community has largely moved to Flux.1 (Black Forest Labs) alongside SD.
- Use ControlNet: To force the image to follow a specific pose or edge map.
Don't:
- Don't use SD 1.5: Unless you need specific legacy LoRAs. SDXL/SD3/Flux are superior.
References
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.
- 4d ago First seen · 45 lines · 17 tokens per session scan A 1e06d6eb5d66
stable-diffusion is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 303 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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image-describe
Describe the contents of an image file or URL in structured detail.
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Generate images via 9Router /v1/images/generations using OpenAI / Gemini Imagen / DALL-E / FLUX / MiniMax / SDWebUI / ComfyUI / Codex models. Use when the user wants to create, generate, draw, or render an image, picture, or text-to-image (txt2img).
9router-video
Generate videos via 9Router /v1/videos/generations using xAI Grok Imagine (grok-imagine-video). Async job flow - submit, poll requestid until done, download MP4. Use when the user wants to create, generate, or render a video, text-to-video (txt2vid), or image-to-video.
9router-tts
Text-to-speech via 9Router /v1/audio/speech using OpenAI / ElevenLabs / Deepgram / Edge TTS / Google TTS / Hyperbolic / Inworld voices. Use when the user wants to convert text to speech, generate audio, voiceover, narrate, or read text aloud.
peon-ping-create-pack
Internal — invoked headlessly by peon create; humans should run peon create instead. Author and render a brand-new PeonPing draft pack — invoked headlessly as: "Use the peon-ping-create-pack skill to draft a pack: name= flavor= vibe= draftroot= . Follow the skill exactly." Authors all 7 CESP categories honoring the…
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Set which voice pack (character voice) plays for the current chat session. Automatically enables sessionoverride rotation mode if not already set. Use when user wants a specific character voice like GLaDOS, Peon, or Kerrigan for this conversation.