Agent skill · data analytics · jeremylongshore
figma-performance-tuning
Optimize Figma REST API performance with caching, partial fetches, and
Why this skill is useful
Provides specific caching strategies and API optimization techniques for Figma that the AI wouldn't reliably generate on its own.
What it needs
Requires figma account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Figma Performance Tuning Overview Optimize Figma REST API performance. Large Figma files can return multi-megabyte JSON responses. Key strategies: fetch only what you need, cache aggressively, and batch requests. Prerequisites Working Figma API integration Understanding of your access patterns (which endpoints, how often) Instructions Step 1: Reduce Payload Size Step 2: Response Caching Step 3: Webhook-Driven Cache Invalidation Step 4: Batch Node Fetches Step 5: Connection Reuse Output Reduced API payload sizes with depth and nodes endpoints Response caching with appropriate TTLs Webhook-driven cache invalidation Batched node fetches reducing request count Connection reuse for lower latency Error Handling Issue Cause Solution ------- ------- ---------- Stale cache No invalidation Use webhooks to invalidate on changes Out of memory Caching full file JSON Use depth=1 or nodes endpoint Slow image exports Large batch, high scale Reduce scale; batch in groups of 50 Expired image URLs Cached URL older than 30 days Set image cache TTL to <24h Examples Measure the win from payload reduction (Step 1) on a real design-system file: Confirm the version-keyed cache (Step 2) short-circuits repeat fetches: Batching and connection reuse details: references/batch-node-fetches.md, references/connection-reuse.md. Resources Figma File Endpoints Figma Rate Limits lru-cache Next Steps For cost optimization, see figma-cost-tuning.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/figma-performance-tuning