GA

gamma-rate-limits

Best practices for handling Gamma API rate limits, including implementing request queuing and exponential backoff.

Install

mkdir -p .claude/skills/gamma-rate-limits && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4734" && unzip -o skill.zip -d .claude/skills/gamma-rate-limits && rm skill.zip

Installs to .claude/skills/gamma-rate-limits

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Understand and manage Gamma API rate limits effectively.
56 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Monitor rate limit headers
  • Build request queuing systems
  • Track usage percentage against plan limits

How it works

It manages API throughput by inspecting rate limit headers and implementing retry logic with exponential backoff or request queuing to prevent 429 errors.

Inputs & outputs

You give it
API request stream
You get back
Rate-limited and queued API calls

When to use gamma-rate-limits

  • Implement exponential backoff for 429 errors
  • Monitor API request headers for usage tracking
  • Build a request queuing system
  • Optimize request frequency for specific plans

About this skill

Gamma Rate Limits

Overview

Understand Gamma API rate limits and implement effective strategies for high-volume usage.

Prerequisites

  • Active Gamma API integration
  • Understanding of HTTP headers
  • Basic queuing concepts

Rate Limit Tiers

PlanRequests/minPresentations/dayExports/hour
Free10510
Pro6050100
Team200200500
EnterpriseCustomCustomCustom

Instructions

Step 1: Check Rate Limit Headers

const response = await gamma.presentations.list();

// Rate limit headers
const headers = response.headers;
console.log('Limit:', headers['x-ratelimit-limit']);
console.log('Remaining:', headers['x-ratelimit-remaining']);
console.log('Reset:', new Date(headers['x-ratelimit-reset'] * 1000));  # 1000: 1 second in ms

Step 2: Implement Exponential Backoff

async function withBackoff<T>(
  fn: () => Promise<T>,
  options = { maxRetries: 5, baseDelay: 1000 }  # 1000: 1 second in ms
): Promise<T> {
  for (let attempt = 0; attempt < options.maxRetries; attempt++) {
    try {
      return await fn();
    } catch (err) {
      if (err.status !== 429 || attempt === options.maxRetries - 1) {  # HTTP 429 Too Many Requests
        throw err;
      }

      const delay = err.retryAfter
        ? err.retryAfter * 1000  # 1 second in ms
        : options.baseDelay * Math.pow(2, attempt);

      console.log(`Rate limited. Retrying in ${delay}ms...`);
      await new Promise(r => setTimeout(r, delay));
    }
  }
  throw new Error('Max retries exceeded');
}

// Usage
const result = await withBackoff(() =>
  gamma.presentations.create({ title: 'My Deck', prompt: 'AI overview' })
);

Step 3: Request Queue

class RateLimitedQueue {
  private queue: Array<() => Promise<any>> = [];
  private processing = false;
  private requestsPerMinute: number;
  private interval: number;

  constructor(requestsPerMinute = 60) {
    this.requestsPerMinute = requestsPerMinute;
    this.interval = 60000 / requestsPerMinute;  # 60000: 1 minute in ms
  }

  async add<T>(fn: () => Promise<T>): Promise<T> {
    return new Promise((resolve, reject) => {
      this.queue.push(async () => {
        try {
          resolve(await fn());
        } catch (err) {
          reject(err);
        }
      });
      this.process();
    });
  }

  private async process() {
    if (this.processing) return;
    this.processing = true;

    while (this.queue.length > 0) {
      const fn = this.queue.shift()!;
      await fn();
      await new Promise(r => setTimeout(r, this.interval));
    }

    this.processing = false;
  }
}

// Usage
const queue = new RateLimitedQueue(30); // 30 req/min

const results = await Promise.all([
  queue.add(() => gamma.presentations.create({ ... })),
  queue.add(() => gamma.presentations.create({ ... })),
  queue.add(() => gamma.presentations.create({ ... })),
]);

Step 4: Monitor Usage

async function getRateLimitStatus() {
  const status = await gamma.rateLimit.status();

  return {
    limit: status.limit,
    remaining: status.remaining,
    percentUsed: ((status.limit - status.remaining) / status.limit * 100).toFixed(1),
    resetAt: new Date(status.reset * 1000),  # 1000: 1 second in ms
    resetIn: Math.ceil((status.reset * 1000 - Date.now()) / 1000),  # 1 second in ms
  };
}

// Usage
const status = await getRateLimitStatus();
console.log(`Used ${status.percentUsed}% of rate limit`);
console.log(`Resets in ${status.resetIn} seconds`);

Output

  • Rate limit aware API calls
  • Automatic retry with backoff
  • Request queuing system
  • Usage monitoring dashboard

Error Handling

ScenarioStrategyImplementation
Occasional 429Exponential backoffwithBackoff() wrapper
Consistent 429Request queueRateLimitedQueue class
Near limitPreemptive throttleCheck remaining before call
Burst trafficToken bucketImplement token bucket algorithm

Resources

Next Steps

Proceed to gamma-security-basics for security best practices.

When not to use it

  • When traffic is consistently below free tier limits

Prerequisites

Active Gamma API integrationUnderstanding of HTTP headersBasic queuing concepts

Limitations

  • Requires integration of custom queue logic
  • Backoff strategies must be tuned to avoid blocking application threads

How it compares

It provides a programmatic queue and backoff wrapper rather than relying on manual request spacing.

Compared to similar skills

gamma-rate-limits side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gamma-rate-limits (this skill)125dNo flagsIntermediate
mcp-builder1363moReviewAdvanced
telegram-mini-app626moReviewAdvanced
stripe-integration482moNo flagsAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

More by jeremylongshore

View all by jeremylongshore

analyzing-logs

jeremylongshore

Analyze application logs to detect performance issues, identify error patterns, and improve stability by extracting key insights.

14123

ollama-setup

jeremylongshore

Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: "install ollama", "local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

1167

backtesting-trading-strategies

jeremylongshore

Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".

1071

generating-database-seed-data

jeremylongshore

Process this skill enables AI assistant to generate realistic test data and database seed scripts for development and testing environments. it uses faker libraries to create realistic data, maintains relational integrity, and allows configurable data volumes. u... Use when working with databases or data models. Trigger with phrases like 'database', 'query', or 'schema'.

1033

cursor-codebase-indexing

jeremylongshore

Execute set up and optimize Cursor codebase indexing. Triggers on "cursor index setup", "codebase indexing", "index codebase", "cursor semantic search". Use when working with cursor codebase indexing functionality. Trigger with phrases like "cursor codebase indexing", "cursor indexing", "cursor".

885

testing-mobile-apps

jeremylongshore

Execute mobile app testing on iOS and Android devices/simulators. Use when performing specialized testing. Trigger with phrases like "test mobile app", "run iOS tests", or "validate Android functionality".

810

You might also like

mcp-builder

anthropics

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

136215

telegram-mini-app

davila7

Expert in building Telegram Mini Apps (TWA) - web apps that run inside Telegram with native-like experience. Covers the TON ecosystem, Telegram Web App API, payments, user authentication, and building viral mini apps that monetize. Use when: telegram mini app, TWA, telegram web app, TON app, mini app.

62163

stripe-integration

wshobson

Implement Stripe payment processing for robust, PCI-compliant payment flows including checkout, subscriptions, and webhooks. Use when integrating Stripe payments, building subscription systems, or implementing secure checkout flows.

48165

copilot-sdk

github

Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.

763

nodejs-backend-patterns

wshobson

Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.

1246

chatgpt-app-builder

mcp-use

Build ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK. Use when creating ChatGPT apps, building MCP servers with widgets, defining React widgets, working with Apps SDK, or when user mentions ChatGPT widgets, mcp-use widgets, or Apps SDK development.

535

Search skills

Search the agent skills registry