adobe-performance-tuning

name	adobe-performance-tuning
description	Optimize Adobe API performance with token caching, async job batching, connection pooling, and response caching for Firefly, PDF Services, and Photoshop API workflows. Trigger with phrases like "adobe performance", "optimize adobe", "adobe latency", "adobe caching", "adobe slow", "adobe batch".
allowed-tools	Read, Write, Edit
version	1.6.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","design","adobe"]
compatibility	Designed for Claude Code

Adobe Performance Tuning

Overview

Optimize Adobe API performance across Firefly Services, PDF Services, and Photoshop APIs. Key bottlenecks include IMS token generation, async job polling overhead, and cold-start latency on serverless platforms.

Prerequisites

• Adobe SDK installed and functional
• Understanding of which APIs your app uses most
• Redis or in-memory cache available (optional)
• Performance monitoring in place

Latency Benchmarks (Real-World)

Operation	P50	P95	P99
IMS Token Generation	200ms	500ms	1s
Firefly Text-to-Image (sync)	5s	12s	20s
Firefly Text-to-Image (async poll)	8s	15s	25s
PDF Extract (10-page doc)	3s	8s	15s
PDF Create from HTML	2s	5s	10s
Photoshop Remove Background	4s	10s	18s
Lightroom Auto Tone	3s	8s	15s

Instructions

Optimization 1: Cache IMS Access Tokens (Biggest Win)

The IMS token endpoint returns tokens valid for 24 hours. Never re-generate per request:

// WRONG: generates new token every call (adds 200-500ms each time)
async function makeRequest() {
  const token = await getAccessToken(); // hits IMS every time
}

// RIGHT: cache token and only refresh when expiring
let tokenCache: { token: string; expiresAt: number } | null = null;

  (): <> {
   (tokenCache && tokenCache. > .() + ) {
     tokenCache.; 
  }
   res =  (, {
    : ,
    : { :  },
    :  ({
      : process..!,
      : process..!,
      : ,
      : process..!,
    }),
  });
   data =  res.();
  tokenCache = { : data., : .() + data. *  };
   tokenCache.;
}