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 n24q02m/claude-plugins --skill image-describegit clone --depth 1 https://github.com/n24q02m/claude-pluginsWrote 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/n24q02m/claude-plugins/image-describe)<a href="https://agentmods.dev/skills/n24q02m/claude-plugins/image-describe"><img src="https://agentmods.dev/badge/skills/n24q02m/claude-plugins/image-describe.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 | $0.00016 | $0.00127 |
| Opus 5 | $0.00008 | $0.00063 |
| Sonnet 5 | $0.00003 | $0.00025 |
| Haiku 4.5 | $0.00002 | $0.00013 |
Grade A, and why
image-describe 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 8d 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.
This is a copy
100% identical to image-describe — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Image Describe Workflow
- If the user provides a local path, upload to a short-lived public URL (or use
file://if client supports). - Call
understandtool withmedia_urls=[url]andprompt="Describe this image in 3 paragraphs: visual composition, subjects, notable details." - Use
provider="gemini"andtier="rich"by default for best quality. - Return the text response with the model ID for attribution.
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.
- 8d ago First seen · 12 lines · 16 tokens per session scan A f358e7e23b09
image-describe is a skill published in the GitHub repository n24q02m/claude-plugins (4 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 127 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-describe, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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A renderer that turns presentation content into a single HTML slide deck that opens directly in a browser. It creates a 16:9 slide sequence with navigation, fullscreen viewing, printing to PDF, and speaker-note controls.
media-audio-gen
An audio-generation skill for turning text into speech, cloning a voice from a sample, dubbing videos into other languages, and creating sound effects.
media-higgsfield-explainer
A Higgsfield workflow for making non-photorealistic narrated explainer videos. It pairs each narration line with a 10-second animated clip and joins the clips into one finished video.
media-higgsfield-identity
A Higgsfield workflow for keeping a person, character, product, or other visual reference consistent across generated images. It chooses between a trained identity model and a one-image reference method.
media-notebooklm-slide-prompt
A prompt builder that turns lecture, class, or seminar notes into instructions for NotebookLM Studio to create slides. It also creates image prompts for each slide using Nano Banana, Google's image-generation model.
media-gpt-image-2-prompt
A prompt builder for GPT-image-2, an OpenAI image-generation model. It turns a short request into six sections covering the subject, action, scene, composition, lighting, style, and text limits.