The Complete Production SOP: How to Deploy and Run Sharp High-Speed Image Processing Pipeline in Autonomous AI Agents

---
name: "sharp-image-processing-pipeline"
description: "Resize, crop, transcode, and optimize raster images at extreme speed with libvips Sharp."
metadata:
  author: "Lovell Fuller"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "resize image with sharp"
  - "convert png to webp sharp"
  - "compress jpeg image sharp"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Sharp High-Speed Image Processing Pipeline
Processes high-volume image assets in Node.js using Sharp: resizing (cover, contain), format transcoding (AVIF, WebP), metadata stripping, progressive JPEG encoding, and color quantization.

## Execution SOP & Rules
1. Process images via streams or buffers: sharp(inputBuffer).resize(800, 600, { fit: 'inside' }).webp({ quality: 80 }).toBuffer().
2. Strip private metadata using .withMetadata({ orientation: undefined }) or omitting .withMetadata() entirely.
3. Use .toFormat('avif', { effort: 4, quality: 75 }) for maximum compression efficiency.
4. Handle animated GIFs/WebPs with sharp(buffer, { animated: true }).

import sharp from 'sharp';

export async function processAsset(input: Buffer, targetWidth: number): Promise<Buffer> {
  return sharp(input, { failOn: 'none', animated: false })
    .resize(targetWidth, null, {
      fit: 'inside',
      withoutEnlargement: true
    })
    .webp({
      quality: 80,
      effort: 4,
      lossless: false
    })
    .toBuffer();
}