Skip to content
Skillv1.0.0

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 "ga

by gabrielmoreira(0) 0 installs
Free
Sign in to install

Free account. Installing gives you the manifest plus copy-paste snippets.

See reviews

About

Imported from gabrielmoreira/agent-skills-mirror (mirrors/repos/jeremylongshore@claude-code-plugins-plus-skills/plugins/saas-packs/gamma-pack/skills/gamma-performance-tuning/SKILL.md). Install upstream with npx skills add gabrielmoreira/agent-skills-mirror --skill gamma-performance-tuning. Copyright stays with the author (MIT).

Gamma Performance Tuning

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.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/gabrielmoreira-agent-skills-mirror-gamma-performance-tuning/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

gabrielmoreira-agent-skills-mirror-gamma-performance-tuning.ocm.jsonjson
{
  "ocm": "1",
  "id": "gabrielmoreira-agent-skills-mirror-gamma-performance-tuning",
  "kind": "skill",
  "name": "gamma-performance-tuning",
  "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\".",
  "publisher": "gabrielmoreira",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "coding"
    ],
    "tags": [
      "skill-md",
      "saas",
      "gamma",
      "api",
      "performance",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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\"."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/gabrielmoreira/agent-skills-mirror",
      "path": "mirrors/repos/jeremylongshore@claude-code-plugins-plus-skills/plugins/saas-packs/gamma-pack/skills/gamma-performance-tuning/SKILL.md",
      "ref": "d5c793801e2fc9c29aa3531805809b3460b19d09",
      "url": "https://github.com/gabrielmoreira/agent-skills-mirror/blob/d5c793801e2fc9c29aa3531805809b3460b19d09/mirrors/repos/jeremylongshore@claude-code-plugins-plus-skills/plugins/saas-packs/gamma-pack/skills/gamma-performance-tuning/SKILL.md",
      "key": "gabrielmoreira/agent-skills-mirror/mirrors/repos/jeremylongshore@claude-code-plugins-plus-skills/plugins/saas-packs/gamma-pack/skills/gamma-performance-tuning/SKILL.md"
    },
    "compatibility": "Designed for Claude Code",
    "allowed_tools": [
      "Read,",
      "Write,",
      "Edit"
    ],
    "license": "MIT"
  },
  "instructions": "# Gamma Performance Tuning\n\n## Output\n\nPublish 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.\n\n## Examples\n\nOptimize a fictional staging deck, compare aggregate load/render metrics, and roll back if a synthetic sharing or accessibility check regresses.\n\n## Overview\n\nOptimize 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 ",
  "cost": {
    "context_tokens": 1887
  }
}

Fetch it by URL: GET /api/v1/registry/gabrielmoreira-agent-skills-mirror-gamma-performance-tuning/manifest?version=1.0.0

Reviews

Star ratings from people who tried it. One review per account; edit yours any time.

No reviews yet. Install it, try it, and be the first to rate it.