Anima Performance Tuning
SkillDev toolsLets your agent speed up Anima code generation using caching and parallelism.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Anima Performance Tuning skill
About this capability
'Optimize Anima code generation performance with caching, parallelism,
What this skill tells your AI
The instructions your AI receives, as published by jeremylongshore/tons-of-skills-marketplace in skills/.curated/anima-performance-tuning/SKILL.md and read by ahel’s review.
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.
Performance Targets
| Operation | Target | Notes |
|---|---|---|
| Single component generation | < 10s | Depends on complexity |
| Batch (10 components) | < 2 min | With rate limit delays |
| Cache hit | < 10ms | File-based cache |
| Full design system (50 components) | < 15 min | Sequential with 6s delays |
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.
Instructions
Step 1: File-Based Generation Cache
// src/performance/cache.ts
import crypto from 'crypto';
import fs from 'fs';
class GenerationCache {
private dir: string;
constructor(cacheDir = '.anima-cache') {
this.dir = cacheDir;
fs.mkdirSync(cacheDir, { recursive: true });
}
private hash(fileKey: string, nodeId: string, settings: any): string {
return crypto.createHash('md5').update(`${fileKey}:${nodeId}:${JSON.stringify(settings)}`).digest('hex');
}
async getOrGenerate(
anima: any,
params: any,
maxAgeMs: number = 3600000, // 1 hour
): Promise<any> {
const key = this.hash(params.fileKey, params.nodesId[0], params.settings);
const path = `${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}/${nodeIds.length} components changed — regenerating`);
return changed;
}
Step 3: Output Size Optimization
// src/performance/output-opt.ts
// Post-process generated code for smaller bundle size
function optimizeOutput(content: string): string {
return content
.replace(/\/\*[\s\S]*?\*\//g, '') // Remove block comments
.replace(/^\s*\/\/.*$/gm, '') // Remove line comments
.replace(/\n{3,}/g, '\n\n') // Collapse multiple blank lines
.trim();
}
Output
- File-based generation cache with TTL
- Incremental generation (only changed components)
- Output size optimization via post-processing
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
Next Steps
For cost optimization, see anima-cost-tuning.
Signals
- GitHub stars
- 3k
- Forks
- 396
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
anima-performance-tuning- Source
- github.com/jeremylongshore/tons-of-skills-marketplace