figma-load-scale
Load test Figma API integrations and plan for scale. Use when benchmarking API throughput, testing rate limit behavior, or planning capacity for high-volume Figma integrations. Trigger with phrases like "figma load test", "figma scale", "figma benchmark", "figma capacity", "figma throughput".
Best use case
figma-load-scale is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Load test Figma API integrations and plan for scale. Use when benchmarking API throughput, testing rate limit behavior, or planning capacity for high-volume Figma integrations. Trigger with phrases like "figma load test", "figma scale", "figma benchmark", "figma capacity", "figma throughput".
Teams using figma-load-scale 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/figma-load-scale/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How figma-load-scale Compares
| Feature / Agent | figma-load-scale | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Load test Figma API integrations and plan for scale. Use when benchmarking API throughput, testing rate limit behavior, or planning capacity for high-volume Figma integrations. Trigger with phrases like "figma load test", "figma scale", "figma benchmark", "figma capacity", "figma throughput".
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.
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SKILL.md Source
# Figma Load & Scale
## Overview
Test and plan for the throughput limits of your Figma API integration. Figma's rate limits use a leaky bucket algorithm -- this skill helps you find the bucket size for your plan tier and design your integration to stay within it.
## Prerequisites
- k6 load testing tool (`brew install k6` or `apt install k6`)
- Figma test PAT (do not load test with production token)
- A test Figma file (not your production design system)
## Instructions
### Step 1: k6 Load Test Script
```javascript
// figma-load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate, Trend } from 'k6/metrics';
const figmaErrors = new Rate('figma_errors');
const figmaLatency = new Trend('figma_latency', true);
export const options = {
scenarios: {
// Test 1: Find your rate limit ceiling
rate_limit_probe: {
executor: 'constant-arrival-rate',
rate: 10, // 10 requests per second
timeUnit: '1s',
duration: '2m',
preAllocatedVUs: 5,
maxVUs: 20,
},
},
thresholds: {
figma_errors: ['rate<0.10'], // Less than 10% errors
figma_latency: ['p(95)<3000'], // P95 under 3 seconds
http_req_duration: ['p(99)<5000'], // P99 under 5 seconds
},
};
const PAT = __ENV.FIGMA_PAT;
const FILE_KEY = __ENV.FIGMA_FILE_KEY;
export default function () {
// Use a lightweight endpoint for rate limit testing
const res = http.get(
`https://api.figma.com/v1/files/${FILE_KEY}?depth=1`,
{
headers: { 'X-Figma-Token': PAT },
tags: { endpoint: 'files' },
}
);
figmaLatency.add(res.timings.duration);
const isError = res.status !== 200;
figmaErrors.add(isError);
check(res, {
'status is 200': (r) => r.status === 200,
'not rate limited': (r) => r.status !== 429,
'latency < 2s': (r) => r.timings.duration < 2000,
});
if (res.status === 429) {
const retryAfter = parseInt(res.headers['Retry-After'] || '60');
console.log(`Rate limited. Retry-After: ${retryAfter}s`);
sleep(retryAfter);
} else {
sleep(0.1); // 100ms between requests
}
}
```
### Step 2: Run Load Tests
```bash
# Probe rate limits
k6 run \
--env FIGMA_PAT="${FIGMA_PAT}" \
--env FIGMA_FILE_KEY="${FIGMA_FILE_KEY}" \
figma-load-test.js
# Export results to JSON for analysis
k6 run \
--env FIGMA_PAT="${FIGMA_PAT}" \
--env FIGMA_FILE_KEY="${FIGMA_FILE_KEY}" \
--out json=results.json \
figma-load-test.js
```
### Step 3: Capacity Planning
```typescript
interface FigmaCapacityPlan {
planTier: string;
measuredLimitPerMinute: number;
currentUsagePerMinute: number;
headroomPercent: number;
recommendation: string;
}
function planCapacity(
measuredLimit: number,
currentUsage: number,
planTier: string
): FigmaCapacityPlan {
const headroom = ((measuredLimit - currentUsage) / measuredLimit) * 100;
let recommendation: string;
if (headroom > 50) {
recommendation = 'Adequate capacity. Monitor monthly.';
} else if (headroom > 20) {
recommendation = 'Approaching limits. Implement caching and batching.';
} else {
recommendation = 'Near capacity. Upgrade plan or reduce request volume.';
}
return {
planTier,
measuredLimitPerMinute: measuredLimit,
currentUsagePerMinute: currentUsage,
headroomPercent: Math.round(headroom),
recommendation,
};
}
```
### Step 4: Scaling Strategies
```typescript
// Strategy 1: Request coalescing
// Multiple callers requesting the same file get a single API call
class RequestCoalescer {
private pending = new Map<string, Promise<any>>();
async get(key: string, fetcher: () => Promise<any>): Promise<any> {
if (this.pending.has(key)) {
return this.pending.get(key)!;
}
const promise = fetcher().finally(() => this.pending.delete(key));
this.pending.set(key, promise);
return promise;
}
}
const coalescer = new RequestCoalescer();
// 10 simultaneous requests for the same file = 1 API call
const results = await Promise.all(
Array(10).fill(null).map(() =>
coalescer.get(fileKey, () => figmaClient.getFile(fileKey))
)
);
// Strategy 2: Stagger requests across time
import PQueue from 'p-queue';
const figmaQueue = new PQueue({
concurrency: 3,
interval: 1000,
intervalCap: 5, // Max 5 requests per second
});
// Strategy 3: Pre-fetch during off-peak hours
// Run design token sync at 3 AM, cache results for the day
```
### Step 5: Benchmark Report Template
```markdown
## Figma API Benchmark Report
**Date:** YYYY-MM-DD
**Plan:** [Starter/Pro/Org/Enterprise]
**Seat:** [Full/Collab/Viewer]
### Rate Limit Findings
| Endpoint | Measured Limit/min | First 429 At | Retry-After |
|----------|-------------------|--------------|-------------|
| GET /v1/files/:key?depth=1 | ~30 | Request #31 | 60s |
| GET /v1/files/:key/nodes | ~30 | Request #32 | 60s |
| GET /v1/images/:key | ~20 | Request #21 | 60s |
### Latency
| Endpoint | P50 | P95 | P99 |
|----------|-----|-----|-----|
| /v1/files (depth=1) | 200ms | 500ms | 1200ms |
| /v1/files (full) | 800ms | 2000ms | 4000ms |
| /v1/images | 300ms | 800ms | 1500ms |
### Recommendations
- Cache file metadata (changes infrequently)
- Use webhooks instead of polling
- Batch node IDs in single requests
- Use `depth=1` unless full tree is needed
```
## Output
- k6 load test measuring actual rate limits
- Capacity plan with headroom analysis
- Scaling strategies implemented
- Benchmark report documented
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| All requests 429'd | Rate too aggressive | Start lower, ramp gradually |
| Inconsistent limits | Shared rate limit bucket | Other services using same token |
| k6 connection errors | Too many parallel VUs | Reduce `preAllocatedVUs` |
| Results vary between runs | Leaky bucket state | Wait 5min between test runs |
## Resources
- [k6 Documentation](https://grafana.com/docs/k6/)
- [Figma Rate Limits](https://developers.figma.com/docs/rest-api/rate-limits/)
- [p-queue](https://github.com/sindresorhus/p-queue)
## Next Steps
For reliability patterns, see `figma-reliability-patterns`.Related Skills
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