Gamma Performance Tuning
SkillDev tools'Optimize Gamma API performance and reduce latency.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by jeremylongshore/tons-of-skills-marketplace in skills/.curated/gamma-performance-tuning/SKILL.md and read by Ahel’s review.
Output
Publish a tuning receipt with baseline/post-change aggregate metrics, change owner, canary result, and rollback state. Do not include source content, viewer data, or tokens.
Examples
Optimize a fictional staging deck, compare aggregate load/render metrics, and roll back if a synthetic sharing or accessibility check regresses.
Overview
Optimize Gamma API integration performance. Gamma's generate-poll-retrieve pattern means most latency is in generation time (10-60s), not API call overhead. Optimize by: reducing poll overhead, parallelizing batch operations, caching results, and choosing the right generation parameters.
Prerequisites
- Working Gamma integration (see
gamma-sdk-patterns) - Understanding of async patterns
- Redis or in-memory cache (recommended)
Performance Characteristics
| Operation | Typical Latency | Notes |
|---|---|---|
POST /generations | 200-500ms | Just starts the generation |
GET /generations/{id} (poll) | 100-300ms | Per poll request |
| Full generation (poll to completion) | 10-60s | Depends on content + cards |
GET /themes | 100-200ms | Cacheable |
GET /folders | 100-200ms | Cacheable |
Instructions
Step 1: Optimize Poll Strategy
// src/gamma/smart-poll.ts
// Adaptive polling: start fast, slow down over time
export async function smartPoll(
gamma: GammaClient,
generationId: string,
opts = { maxTimeMs: 180000 }
): Promise<GenerateResult> {
const deadline = Date.now() + opts.maxTimeMs;
let interval = 2000; // Start at 2s
while (Date.now() < deadline) {
const result = await gamma.poll(generationId);
if (result.status === "completed") return result;
if (result.status === "failed") throw new Error("Generation failed");
// Adaptive backoff: poll faster early, slower later
await new Promise((r) => setTimeout(r, interval));
interval = Math.min(interval * 1.5, 10000); // Max 10s between polls
}
throw new Error(`Poll timeout after ${opts.maxTimeMs}ms`);
}
Step 2: Cache Static Data
// src/gamma/cache.ts
import NodeCache from "node-cache";
const cache = new NodeCache({ stdTTL: 3600 }); // 1 hour for static data
export async function getCachedThemes(gamma: GammaClient) {
const key = "gamma:themes";
const cached = cache.get(key);
if (cached) return cached;
const themes = await gamma.listThemes();
cache.set(key, themes);
return themes;
}
export async function getCachedFolders(gamma: GammaClient) {
const key = "gamma:folders";
const cached = cache.get(key);
if (cached) return cached;
const folders = await gamma.listFolders();
cache.set(key, folders);
return folders;
}
// Cache generation results (useful for showing status)
export async function cacheGenerationResult(
generationId: string,
result: GenerateResult
) {
cache.set(`gamma:gen:${generationId}`, result, 86400); // 24 hours
}
Step 3: Parallel Batch Generation
// src/gamma/batch.ts
import pLimit from "p-limit";
const limit = pLimit(3); // Max 3 concurrent generations
export async function batchGenerate(
gamma: GammaClient,
requests: Array<{ content: string; exportAs?: string }>
): Promise<Array<{ index: number; result?: GenerateResult; error?: string }>> {
const results = await Promise.allSettled(
requests.map((req, index) =>
limit(async () => {
const { generationId } = await gamma.generate({
content: req.content,
outputFormat: "presentation",
exportAs: req.exportAs,
});
const result = await smartPoll(gamma, generationId);
return { index, result };
})
)
);
return results.map((r, i) => {
if (r.status === "fulfilled") return r.value;
return { index: i, error: (r.reason as Error).message };
});
}
Step 4: Reduce Generation Time
// Shorter content = faster generation
// "brief" text = fewer AI-generated words per card = faster
// SLOWER: extensive text on many cards
await gamma.generate({
content: "Comprehensive 20-card guide to machine learning...",
outputFormat: "presentation",
textAmount: "extensive", // More text per card = slower
});
// FASTER: brief text, fewer implied cards
await gamma.generate({
content: "5-card overview of ML basics: supervised, unsupervised, reinforcement, deep learning, applications",
outputFormat: "presentation",
textAmount: "brief", // Less text per card = faster
});
// FASTEST: preserve mode (no AI text generation)
await gamma.generate({
content: "Your pre-written slide content here...",
outputFormat: "presentation",
textMode: "preserve", // Uses your text as-is, no AI rewriting
});
Step 5: Preload Data at Startup
// src/gamma/preload.ts
// Fetch themes and folders at app startup, not per-request
let preloaded = false;
export async function preloadGammaData(gamma: GammaClient) {
if (preloaded) return;
const [themes, folders] = await Promise.all([
gamma.listThemes(),
gamma.listFolders(),
]);
// Cache for the session
cache.set("gamma:themes", themes, 0); // No TTL (until restart)
cache.set("gamma:folders", folders, 0);
preloaded = true;
console.log(`Preloaded ${themes.length} themes, ${folders.length} folders`);
}
Step 6: Connection Keep-Alive
// src/gamma/optimized-client.ts
import http from "node:http";
import https from "node:https";
// Reuse TCP connections
const agent = new https.Agent({
keepAlive: true,
maxSockets: 10,
keepAliveMsecs: 60000,
});
export function createOptimizedClient(apiKey: string) {
const base = "https://public-api.gamma.app/v1.0";
const headers = { "X-API-KEY": apiKey, "Content-Type": "application/json" };
async function request(method: string, path: string, body?: unknown) {
const res = await fetch(`${base}${path}`, {
method, headers,
body: body ? JSON.stringify(body) : undefined,
// @ts-ignore — agent support in Node.js
agent,
});
if (!res.ok) throw new Error(`Gamma ${res.status}`);
return res.json();
}
return {
generate: (body: any) => request("POST", "/generations", body),
poll: (id: string) => request("GET", `/generations/${id}`),
listThemes: () => request("GET", "/themes"),
listFolders: () => request("GET", "/folders"),
};
}
Performance Targets
| Operation | Target | Action if Exceeded |
|---|---|---|
| Theme/folder lookup | < 50ms (cached) | Verify cache hit |
| Generation start | < 500ms | Check network latency |
| Full generation (5 cards) | < 30s | Use textAmount: "brief" |
| Full generation (10+ cards) | < 60s | Split into smaller decks |
| Batch of 10 presentations | < 3 min | Use concurrency limit of 3 |
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| High latency on first request | Cold TCP connection | Use keep-alive agent |
| Cache miss storm | Cache expired simultaneously | Stagger TTLs |
| Batch rate limiting | Too many concurrent requests | Reduce p-limit concurrency |
| Poll timeout | Complex generation | Increase timeout, simplify content |
Resources
Next Steps
Proceed to gamma-cost-tuning for credit optimization.
Signals
- GitHub stars
- 3k
- Forks
- 415
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
gamma-performance-tuning- Source
- github.com/jeremylongshore/tons-of-skills-marketplace
github.com/jeremylongshore/tons-of-skills-marketplace
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