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anima-performance-tuning

'Optimize Anima code generation performance with caching, parallelism,

Install / Use

npx skills add jeremylongshore/tons-of-skills-marketplace --skill anima-performance-tuning

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Category

Design

Supported Platforms

Universal

Our assessment of anima-performance-tuning

anima-performance-tuning scores 88/100 on our quality scale, 98th of 297 Design skills we index (top 33%).

Its SKILL.md is 6.8 KB long, well organised into 14 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.

With 2,785 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
18/20
Description
12/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so anima-performance-tuning is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

anima-performance-tuning compared with similar skills

All 4 of these similar skills score higher than anima-performance-tuning; compare them before choosing.

SkillScoreStarsUpdatedFormat
anima-performance-tuning (this skill)by jeremylongshore882.8k6d agoSKILL.md
algorithmic-artby anthropics100177.9k7d agoSKILL.md
pptxby anthropics100177.9k7d agoSKILL.md
designby nextlevelbuilder100130.2k9d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k9d agoSKILL.md

Frequently asked questions

How do I install anima-performance-tuning?
Run npx skills add jeremylongshore/tons-of-skills-marketplace --skill anima-performance-tuning. The install tabs above show the steps for each supported agent.
Which AI agents does anima-performance-tuning work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is anima-performance-tuning safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is anima-performance-tuning still maintained?
The repository was last updated 6 days ago, so anima-performance-tuning is actively maintained.

name: anima-performance-tuning description: 'Optimize Anima code generation performance with caching, parallelism, and output tuning.

Use when reducing generation latency, optimizing batch component generation,

or improving generated code quality for production use.

Trigger with: "anima performance", "anima slow", "anima optimization", "anima caching".

' allowed-tools: Read, Write, Edit, Bash(npm:*) version: 2.0.0 argument-hint: "[generation-workload]" model: inherit effort: high license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

  • saas
  • design
  • figma
  • anima
  • performance compatibility: Requires Node.js 20+, approved Anima API access, current Anima SDK documentation, and authorized Figma or website source access

Anima Performance Tuning

Overview

Improve design-to-code throughput without treating cache hits or smaller output as success unless the result still matches the approved design version, accessibility expectations, and project build contract.

Measurement Contract

Record source-fetch, queue, generation, asset, validation, and review durations separately. Establish targets from the team's own representative fixtures and provider agreement; do not present illustrative latency or quota numbers as an Anima service-level objective.

Prerequisites

  • A representative staging fixture and a baseline measurement of generation duration, cache hit rate, failure rate, and generated-code validation result.
  • A version-aware cache key and retention policy that ties each artifact to Figma source version, node ID, and generation settings.
  • Review gates for generated output so performance changes cannot automatically replace approved components or strip required licenses/accessibility content.

Authentication

Load ANIMA_TOKEN and the source-scoped FIGMA_TOKEN only in the backend worker. Performance tests use synthetic allowlisted sources; do not broaden credentials or retry authorization failures to make a benchmark complete.

Instructions

Step 1: File-Based Generation Cache

// src/performance/cache.ts
import crypto from 'crypto';
import fs from 'fs';
import { Anima } from '@animaapp/anima-sdk';

class GenerationCache {
  private dir: string;

  constructor(cacheDir = '.anima-cache') {
    this.dir = cacheDir;
    fs.mkdirSync(cacheDir, { recursive: true });
  }

  private hash(fileKey: string, sourceRevision: string, nodeId: string, settings: object): string {
    return crypto.createHash('sha256').update(`${fileKey}:${sourceRevision}:${nodeId}:${JSON.stringify(settings)}`).digest('hex');
  }

  async getOrGenerate(
    anima: Anima,
    params: Parameters<Anima['generateCode']>[0],
    sourceRevision: string,
    maxAgeMs: number = 3600000, // 1 hour
  ): Promise<Awaited<ReturnType<Anima['generateCode']>>> {
    const key = this.hash(params.fileKey, sourceRevision, params.nodesId[0], params.settings);
    const path = `${this.dir}/${key}.json`;

    if (fs.existsSync(path)) {
      const stat = fs.statSync(path);
      if (Date.now() - stat.mtimeMs < maxAgeMs) {
        return JSON.parse(fs.readFileSync(path, 'utf8'));
      }
    }

    const result = await anima.generateCode(params);
    fs.writeFileSync(path, JSON.stringify(result));
    return result;
  }

  clearOlderThan(maxAgeMs: number): number {
    let cleared = 0;
    for (const file of fs.readdirSync(this.dir)) {
      const path = `${this.dir}/${file}`;
      if (Date.now() - fs.statSync(path).mtimeMs > maxAgeMs) {
        fs.unlinkSync(path);
        cleared++;
      }
    }
    return cleared;
  }
}

export { GenerationCache };

Step 2: Incremental Generation (Only Changed Components)

// src/performance/incremental.ts
// Only regenerate components whose Figma nodes changed

async function getNodeLastModified(fileKey: string, nodeId: string): Promise<string> {
  const res = await fetch(
    `https://api.figma.com/v1/files/${fileKey}/nodes?ids=${nodeId}`,
    { headers: { 'X-Figma-Token': process.env.FIGMA_TOKEN! } }
  );
  const data = await res.json();
  return data.lastModified;
}

async function generateOnlyChanged(
  anima: any,
  fileKey: string,
  nodeIds: string[],
  lastModifiedCache: Map<string, string>,
): Promise<string[]> {
  const changed: string[] = [];

  for (const nodeId of nodeIds) {
    const lastMod = await getNodeLastModified(fileKey, nodeId);
    if (lastMod !== lastModifiedCache.get(nodeId)) {
      changed.push(nodeId);
      lastModifiedCache.set(nodeId, lastMod);
    }
  }

  console.log(`${changed.length}/${nodeIds.length} components changed — regenerating`);
  return changed;
}

Step 3: Validate Output Without Semantic Rewriting

// Preserve generated semantics; measure before applying reviewed transforms.
function recordOutput(fileName: string, content: string) {
  return {
    fileName,
    bytes: Buffer.byteLength(content),
    digest: crypto.createHash('sha256').update(content).digest('hex'),
  };
}

Tool Discipline

Use Read and Grep to inspect the existing integration and generated diff before changing anything. Use Write or Edit only inside the approved generated-code, test, or configuration paths. Use the declared Bash commands only for the explicit install, validation, or diagnostic steps in this workflow; never print tokens, source designs, generated source, or private website captures.

Output

  • File-based generation cache with TTL
  • Incremental generation (only changed components)
  • Output size and digest measurements without destructive rewriting

Examples

Benchmark ten approved staging components once without cache and once with the cache keyed by source version, node ID, and settings. Compare duration, API calls, output size, lint/type results, and visual review rather than just cache hit rate. Regenerate only components whose recorded source version changed, and keep the prior generated artifact available for diff review. If a cache entry cannot prove its source version, post-processing changes required behavior, or rate limits increase, disable the optimization and return to the prior validated generation path while investigating the aggregate measurements.

Error Handling

| Failure | Response | |---------|----------| | Cache artifact lacks valid source/version metadata | Refuse reuse and regenerate the approved component. | | Incremental detector cannot determine change state | Treat the affected component as needing controlled regeneration. | | Optimizer changes semantics or removes required content | Revert the post-processing rule and restore the reviewed artifact. | | Throughput increases provider failures or rate limits | Reduce concurrency, apply bounded backoff, and preserve user-visible job state. |

Resources

Related Skills

View on GitHub
GitHub Stars2.8k
CategoryDesign
Updated6d ago
Forks404

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions