anima-performance-tuning

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

1,868 stars

Best use case

anima-performance-tuning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using anima-performance-tuning should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/anima-performance-tuning/SKILL.md --create-dirs "https://raw.githubusercontent.com/jeremylongshore/claude-code-plugins-plus-skills/main/plugins/saas-packs/anima-pack/skills/anima-performance-tuning/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/anima-performance-tuning/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How anima-performance-tuning Compares

Feature / Agentanima-performance-tuningStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Anima Performance Tuning

## 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 |

## Instructions

### Step 1: File-Based Generation Cache

```typescript
// 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)

```typescript
// 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

```typescript
// 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

## Resources

- [Anima API](https://docs.animaapp.com/docs/anima-api)
- [Figma API Nodes](https://www.figma.com/developers/api#get-file-nodes-endpoint)

## Next Steps

For cost optimization, see `anima-cost-tuning`.

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