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 commands/mfmezger/ai_agent_dotfiles/generate-imagegit clone --depth 1 https://github.com/mfmezger/ai_agent_dotfilesWrote 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/mfmezger/ai_agent_dotfiles/generate-image)<a href="https://agentmods.dev/commands/mfmezger/ai_agent_dotfiles/generate-image"><img src="https://agentmods.dev/badge/commands/mfmezger/ai_agent_dotfiles/generate-image.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 5.1 | $0.00049 | $0.01289 |
| Opus 5 | $0.00024 | $0.00645 |
| Sonnet 5 | $0.00010 | $0.00258 |
| Haiku 4.5 | $0.00005 | $0.00129 |
Grade A, and why
generate-image 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 5d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation with Gemini
Generate images using Google's Gemini image generation model via Vertex AI. Supports both text-to-image and image-to-image generation.
Prerequisites
Environment variables (can be in .env file in working directory):
GOOGLE_APPLICATION_CREDENTIALS- Path to service account JSON fileGOOGLE_CLOUD_PROJECT- GCP project ID
Usage
Run the generation script using uvx:
# Text-to-image generation
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "PROMPT"
# Image-to-image generation (with input image)
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "PROMPT" -i /path/to/input/image.png
Options
| Flag | Description | Default |
|---|---|---|
-i, --input PATH |
Input image for image-to-image generation | None |
-o, --output PATH |
Where to save the image | Temp file |
--aspect-ratio |
"16:9", "1:1", "9:16" | "16:9" |
--size |
"1K" or "2K" | "2K" |
Examples
# Basic text-to-image - saves to temp file, prints path
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "A serene mountain landscape at sunset"
# Save to specific location
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "A cute robot" -o ./robot.png
# Square format
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "Abstract art" --aspect-ratio 1:1
# High resolution
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "Detailed cityscape" --size 2K
# Image-to-image: Transform an existing image
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "Make this scene nighttime with stars" -i ./daytime_photo.jpg
# Image-to-image: Add elements to an image
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "Add a rainbow in the sky" -i ./landscape.png -o ./landscape_with_rainbow.png
# Image-to-image: Style transfer
uvx --with google-genai --with python-dotenv python ~/.claude/skills/generate-image/scripts/generate.py "Convert to watercolor painting style" -i ./photo.jpg
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.
- 5d ago First seen · 112 lines · 49 tokens per session scan A d0c586d2aa5b
generate-image is a command published in the GitHub repository mfmezger/ai_agent_dotfiles (5 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,289 once invoked, about $0.0002 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 2026-08-31.
Other commands, from other repositories
psm
/oh-my-claudecode:psm is a compatibility alias for /oh-my-claudecode:project-session-manager.
harness
Take one harness from "wired" to "the agent is visibly smarter for it".
release
Ship a stable cmux release built by CI: bump version, update changelog, open a PR, merge, tag, then GitHub Actions builds, signs, and publishes.
step-research
Always research before proposing a fix. The Untether bug you're chasing is often a known upstream engine quirk, a previously-fixed regression, or a documented config gotcha.
root-cause
Use when any test fails, bug appears, or behaviour surprises you, before proposing a fix - find the cause and prove it, by reading real evidence, tracing bad values back to their origin, comparing against a working case, and testing one hypothesis at a time.
research
Research a technical or product question.