appfolio-performance-tuning
Optimize AppFolio API performance with caching and batch operations. Trigger: "appfolio performance".
Best use case
appfolio-performance-tuning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Optimize AppFolio API performance with caching and batch operations. Trigger: "appfolio performance".
Teams using appfolio-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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/appfolio-performance-tuning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How appfolio-performance-tuning Compares
| Feature / Agent | appfolio-performance-tuning | 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?
Optimize AppFolio API performance with caching and batch operations. Trigger: "appfolio performance".
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
# AppFolio Performance Tuning
## Overview
AppFolio's property management API handles bulk tenant queries, property portfolio pagination, and work order batch processing. Large portfolios with thousands of units generate heavy read traffic on listing endpoints. Optimizing cache lifetimes for slow-changing property data, batching work order updates, and pooling HTTP connections reduces API call volume by 60-80% and cuts dashboard load times from seconds to sub-second.
## Caching Strategy
```typescript
const cache = new Map<string, { data: any; expiry: number }>();
const TTL = { properties: 300_000, tenants: 120_000, units: 300_000, workOrders: 60_000 };
async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) {
const entry = cache.get(key);
if (entry && entry.expiry > Date.now()) return entry.data;
const data = await fn();
cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] });
return data;
}
```
## Batch Operations
```typescript
async function batchWorkOrders(client: any, ids: string[], batchSize = 25) {
const results = [];
for (let i = 0; i < ids.length; i += batchSize) {
const batch = ids.slice(i, i + batchSize);
const res = await Promise.all(batch.map(id => client.http.get(`/work_orders/${id}`)));
results.push(...res.map(r => r.data));
if (i + batchSize < ids.length) await new Promise(r => setTimeout(r, 200));
}
return results;
}
```
## Connection Pooling
```typescript
import { Agent } from 'https';
const agent = new Agent({ keepAlive: true, maxSockets: 10, maxFreeSockets: 5, timeout: 30_000 });
// Pass to axios/fetch: { httpsAgent: agent }
```
## Rate Limit Management
```typescript
async function withRateLimit(fn: () => Promise<any>): Promise<any> {
const res = await fn();
const remaining = parseInt(res.headers['x-ratelimit-remaining'] || '100');
if (remaining < 5) {
const retryAfter = parseInt(res.headers['retry-after'] || '2') * 1000;
await new Promise(r => setTimeout(r, retryAfter));
}
return res;
}
```
## Monitoring
```typescript
const metrics = { apiCalls: 0, cacheHits: 0, errors: 0, totalLatency: 0 };
function track(startMs: number, hit: boolean, error?: boolean) {
metrics.apiCalls++; metrics.totalLatency += Date.now() - startMs;
if (hit) metrics.cacheHits++; if (error) metrics.errors++;
}
// Log: avg latency, cache hit rate, error rate per minute
```
## Performance Checklist
- [ ] Cache property and unit listings with 5-min TTL
- [ ] Use incremental sync via last_modified timestamps
- [ ] Batch work order updates in groups of 25
- [ ] Enable HTTP keep-alive with connection pooling
- [ ] Parse rate limit headers and back off proactively
- [ ] Parallelize independent dashboard queries with Promise.all
- [ ] Monitor cache hit ratio (target > 70%)
- [ ] Set request timeouts to 30s to avoid hung connections
## Error Handling
| Issue | Cause | Fix |
|-------|-------|-----|
| 429 Too Many Requests | Exceeded API rate limit | Parse Retry-After header, exponential backoff |
| Stale tenant data | Cache TTL too long | Reduce tenant cache to 2 min, add cache-bust on writes |
| Timeout on portfolio list | Large dataset with no pagination | Add page_size=100 and cursor-based iteration |
| Connection reset | Socket exhaustion | Enable keep-alive agent with maxSockets cap |
## Resources
- [AppFolio Stack APIs](https://www.appfolio.com/stack/partners/api)
- [AppFolio Engineering Blog](https://engineering.appfolio.com)
## Next Steps
See `appfolio-reference-architecture`.Related Skills
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