AGENT SKILL

gamma-performance-tuning

Optimize Gamma API performance and reduce latency. Use when experiencing slow response times, optimizing throughput, or improving user experience with Gamma integrations. Trigger with phrases like "gamma performance", "gamma slow", "gamma latency", "gamma optimization", "gamma speed".

License 许可
MIT
最近更新
2026-03-20
Stars
★ 4
Forks
0

01 前言 Front Matter

name
gamma-performance-tuning
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
version
1.0.0
description
Optimize Gamma API performance and reduce latency. Use when experiencing slow response times, optimizing throughput, or improving user experience with Gamma integrations. Trigger with phrases like "gamma performance", "gamma slow", "gamma latency", "gamma optimization", "gamma speed".
allowed-tools
Read, Write, Edit
compatible-with
claude-code, codex, openclaw

02 资源文件 1

03 SKILL.md 正文

Gamma Performance Tuning

Contents

Overview

Optimize Gamma API integration performance through client configuration, caching, connection pooling, and parallel request patterns.

Prerequisites

  • Working Gamma integration
  • Performance monitoring tools
  • Understanding of caching concepts

Instructions

Step 1: Optimize Client Configuration

Enable keep-alive, compression, and configure max sockets (10). Set retry conditions for 5xx and 429 errors.

Step 2: Implement Response Caching

Use node-cache with 5-minute TTL. Invalidate on presentation.updated events.

Step 3: Parallelize Requests

Replace sequential loops with p-limit (concurrency 5) for bulk operations. Use batch API where available.

Step 4: Add Pagination with Generators

Use async generators for memory-efficient iteration over large presentation lists.

Step 5: Optimize Request Payloads

Request only needed fields to reduce response size. Use returnImmediately for creation operations.

Step 6: Configure Connection Pooling

Create shared HTTP/HTTPS agents with keep-alive, 25 max sockets, and 60s timeout.

See detailed implementation for advanced patterns.

Output

  • Optimized client configuration
  • Response caching layer
  • Parallel request patterns
  • Connection pooling setup
  • Performance monitoring

Error Handling

Issue Cause Solution
High latency No connection reuse Enable keep-alive and pooling
Memory growth Unbounded cache Set TTL and max cache size
Rate limiting Too many parallel requests Use p-limit with concurrency cap

Examples

Performance Targets

Operation Target Action if Exceeded
Simple GET < 200ms Check network, use caching
List (100 items) < 500ms Reduce page size
Create presentation < 5s Use async pattern
Export PDF < 30s Use webhook notification

Resources

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