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 AIDotNet/MoYuCode --skill sharpgit clone --depth 1 https://github.com/AIDotNet/MoYuCodeWrote 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/aidotnet/moyucode/sharp)<a href="https://agentmods.dev/skills/aidotnet/moyucode/sharp"><img src="https://agentmods.dev/badge/skills/aidotnet/moyucode/sharp/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/aidotnet/moyucode/sharp"><img src="https://agentmods.dev/badge/skills/aidotnet/moyucode/sharp.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 | $0.00033 | $0.00881 |
| Opus 5.5 | $0.00013 | $0.00352 |
| Sonnet 5.5 | $0.00007 | $0.00176 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
sharp 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 1mo 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sharp Tool
Description
High-performance image processing for resizing, converting, and manipulating images.
Source
- Repository: lovell/sharp
- License: Apache-2.0
Installation
npm install sharp
Usage Examples
Resize Image
import sharp from 'sharp';
// Resize to specific dimensions
await sharp('input.jpg')
.resize(800, 600)
.toFile('output.jpg');
// Resize with aspect ratio preserved
await sharp('input.jpg')
.resize(800, null) // Width 800, auto height
.toFile('output.jpg');
// Resize with fit options
await sharp('input.jpg')
.resize(800, 600, {
fit: 'cover', // cover, contain, fill, inside, outside
position: 'center' // center, top, right, bottom, left
})
.toFile('output.jpg');
Convert Format
// Convert to WebP
await sharp('input.jpg')
.webp({ quality: 80 })
.toFile('output.webp');
// Convert to AVIF (modern format)
await sharp('input.jpg')
.avif({ quality: 60 })
.toFile('output.avif');
// Convert to PNG with transparency
await sharp('input.jpg')
.png({ compressionLevel: 9 })
.toFile('output.png');
Image Manipulation
// Rotate and flip
await sharp('input.jpg')
.rotate(90)
.flip()
.toFile('output.jpg');
// Blur and sharpen
await sharp('input.jpg')
.blur(5)
.sharpen()
.toFile('output.jpg');
// Grayscale and tint
await sharp('input.jpg')
.grayscale()
.tint({ r: 255, g: 128, b: 0 })
.toFile('output.jpg');
// Crop
await sharp('input.jpg')
.extract({ left: 100, top: 100, width: 500, height: 300 })
.toFile('output.jpg');
Add Watermark
async function addWatermark(input: string, watermark: string, output: string) {
const image = sharp(input);
const { width, height } = await image.metadata();
// Resize watermark
const watermarkBuffer = await sharp(watermark)
.resize(Math.round(width! * 0.2))
.toBuffer();
await image
.composite([{
input: watermarkBuffer,
gravity: 'southeast',
blend: 'over',
}])
.toFile(output);
}
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.
- 1mo ago First seen · 157 lines · 33 tokens per session scan A c21043993c56
sharp is a skill published in the GitHub repository AIDotNet/MoYuCode (85 stars, last pushed 8mo ago), licensed MIT. It adds 33 tokens to every session and 881 once invoked, about $0.0001 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.
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