Agent skill

Gamma Performance Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize Gamma API performance and reduce latency. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedFrontend & Design

Install Gamma Performance Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill gamma-performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace gamma-performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/gamma-performance-tuning .claude/skills/gamma-performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gamma-performance-tuning
GitHub stars
2.8k
Token cost
~2k tokens
SKILL.md length
275 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize Gamma API performance and reduce latency. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 6 steps: Optimize Poll Strategy → Cache Static Data → Parallel Batch Generation → …
  • Experiencing slow response times
  • SKILL.md covers Output, Examples, Overview and Prerequisites, plus 6 more sections
  • Reaches public-api.gamma.app

What it does

Gamma Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. 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".

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in Frontend & Design, covering UX design. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Experiencing slow response times
  • Optimizing throughput
  • Improving user experience with Gamma integrations
  • With phrases like gamma performance

Example prompts

  • “gamma performance”
  • “gamma slow”
  • “gamma latency”
  • “/gamma-performance-tuning”

Requirements

  • Node.js
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Optimize Poll Strategy
  2. Cache Static Data
  3. Parallel Batch Generation
  4. Reduce Generation Time
  5. Preload Data at Startup
  6. Connection Keep-Alive

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • public-api.gamma.app

    Also links to:

    • github.com
    • developers.gamma.app

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Gamma Performance Tuning loads about 2k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 275 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 275 words, ~2,026 tokens.

Download SKILL.mdSave it as .claude/skills/gamma-performance-tuning/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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".
allowed-tools
Read, Write, Edit
compatibility
Designed for Claude Code
version
1.13.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, gamma, api, performance

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

OperationTypical LatencyNotes
POST /generations200-500msJust starts the generation
GET /generations/{id} (poll)100-300msPer poll request
Full generation (poll to completion)10-60sDepends on content + cards
GET /themes100-200msCacheable
GET /folders100-200msCacheable

Instructions

Step 1: Optimize Poll Strategy
typescript
// 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
typescript
// 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
typescript
// 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
typescript
// 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
typescript
// 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
typescript
// 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

OperationTargetAction if Exceeded
Theme/folder lookup< 50ms (cached)Verify cache hit
Generation start< 500msCheck network latency
Full generation (5 cards)< 30sUse textAmount: "brief"
Full generation (10+ cards)< 60sSplit into smaller decks
Batch of 10 presentations< 3 minUse concurrency limit of 3

Error Handling

IssueCauseSolution
High latency on first requestCold TCP connectionUse keep-alive agent
Cache miss stormCache expired simultaneouslyStagger TTLs
Batch rate limitingToo many concurrent requestsReduce p-limit concurrency
Poll timeoutComplex generationIncrease timeout, simplify content

Resources

Next Steps

Proceed to gamma-cost-tuning for credit optimization.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/gamma-performance-tuning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

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Questions about Gamma Performance Tuning

What does Gamma Performance Tuning do?

Optimize Gamma API performance and reduce latency. An agent skill from jeremylongshore/tons-of-skills-marketplace. Gamma Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Gamma API performance and reduce latency.

When should I use Gamma Performance Tuning?

Gamma Performance Tuning fits situations like: experiencing slow response times; optimizing throughput; improving user experience with Gamma integrations; with phrases like gamma performance.

How do I install Gamma Performance Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill gamma-performance-tuning -a claude-code`. Or copy the skill folder (skills/.curated/gamma-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/gamma-performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Gamma Performance Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill gamma-performance-tuning -a codex`. Or copy the skill folder (skills/.curated/gamma-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/gamma-performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Gamma Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill gamma-performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gamma-performance-tuning, .gemini/skills/gamma-performance-tuning, .github/skills/gamma-performance-tuning and .opencode/skills/gamma-performance-tuning in your project.

What does Gamma Performance Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Gamma Performance Tuning is instructions for the agent only. Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Write, Edit. Compatibility (from SKILL.md): Designed for Claude Code.

Does Gamma Performance Tuning access the network?

SKILL.md names 3 domains. In commands or code: public-api.gamma.app; the agent is likely to contact it when it follows the instructions. As links in the text: github.com and developers.gamma.app. This is read from the text; nothing was executed.

Is Gamma Performance Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gamma Performance Tuning use?

Gamma Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gamma Performance Tuning use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 765 tokens, read only when the agent opens those files.

What are the alternatives to Gamma Performance Tuning?

Skills that share tags, products or a category with Gamma Performance Tuning: Impeccable (bestofjs/bestofjs, 3.1k stars), Interface Design for Dashboards and Apps (holaboss-ai/holaOS, 11k stars), Animate (growupanand/ConvoForm, 102 stars) and Migrate Content Ia (docker/docs, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gamma Performance Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.