maintainx-performance-tuning
Provides optimization patterns like caching and keep-alive agents for MaintainX API integrations.
Install
mkdir -p .claude/skills/maintainx-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8852" && unzip -o skill.zip -d .claude/skills/maintainx-performance-tuning && rm skill.zipInstalls to .claude/skills/maintainx-performance-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Optimize MaintainX API integration performance.Key capabilities
- →Implement connection pooling with keep-alive
- →Configure multi-level caching using memory and Redis
- →Batch and deduplicate API requests with DataLoader
- →Optimize data fetching with cursor-based pagination
- →Prevent redundant concurrent API calls with request deduplication
How it works
The skill implements connection pooling to reuse TCP connections and a multi-level cache strategy to reduce redundant API calls. It also uses DataLoader to batch multiple requests for the same entity into a single operation.
Inputs & outputs
When to use maintainx-performance-tuning
- →Reducing API latency in MaintainX
- →Optimizing data fetching patterns
- →Implementing request deduplication
- →Improving throughput for CMMS integrations
About this skill
MaintainX Performance Tuning
Overview
Optimize MaintainX integration performance with caching, connection pooling, efficient pagination, and request deduplication.
Prerequisites
- MaintainX integration working
- Node.js 18+
- Redis (recommended for production caching)
- Performance baseline measurements
Instructions
Step 1: Connection Pooling with Keep-Alive
// src/performance/pooled-client.ts
import axios from 'axios';
import http from 'node:http';
import https from 'node:https';
// Reuse TCP connections instead of opening new ones per request
const httpAgent = new http.Agent({ keepAlive: true, maxSockets: 10 });
const httpsAgent = new https.Agent({ keepAlive: true, maxSockets: 10 });
const client = axios.create({
baseURL: 'https://api.getmaintainx.com/v1',
headers: {
Authorization: `Bearer ${process.env.MAINTAINX_API_KEY}`,
'Content-Type': 'application/json',
},
httpAgent,
httpsAgent,
timeout: 30_000,
});
// Benefit: Eliminates TCP handshake + TLS negotiation per request
// Typical improvement: 100-200ms saved per request
Step 2: Multi-Level Caching
// src/performance/cache.ts
interface CacheLayer<T> {
get(key: string): Promise<T | undefined>;
set(key: string, value: T, ttlMs: number): Promise<void>;
}
// L1: In-memory (fastest, per-process)
class MemoryCache<T> implements CacheLayer<T> {
private store = new Map<string, { value: T; expiresAt: number }>();
async get(key: string) {
const entry = this.store.get(key);
if (entry && entry.expiresAt > Date.now()) return entry.value;
this.store.delete(key);
return undefined;
}
async set(key: string, value: T, ttlMs: number) {
this.store.set(key, { value, expiresAt: Date.now() + ttlMs });
}
}
// L2: Redis (shared across processes)
class RedisCache<T> implements CacheLayer<T> {
constructor(private redis: any) {}
async get(key: string) {
const data = await this.redis.get(`mx:${key}`);
return data ? JSON.parse(data) : undefined;
}
async set(key: string, value: T, ttlMs: number) {
await this.redis.setex(`mx:${key}`, Math.ceil(ttlMs / 1000), JSON.stringify(value));
}
}
// Multi-level cache: check L1 first, then L2, then fetch
class MultiCache<T> {
constructor(private l1: CacheLayer<T>, private l2: CacheLayer<T>) {}
async getOrFetch(key: string, ttlMs: number, fetcher: () => Promise<T>): Promise<T> {
// Check L1
let value = await this.l1.get(key);
if (value !== undefined) return value;
// Check L2
value = await this.l2.get(key);
if (value !== undefined) {
await this.l1.set(key, value, ttlMs / 2); // L1 shorter TTL
return value;
}
// Fetch from API
value = await fetcher();
await this.l1.set(key, value, ttlMs / 2);
await this.l2.set(key, value, ttlMs);
return value;
}
}
Step 3: DataLoader for Batch Loading
When multiple parts of your app need the same work order, batch and deduplicate:
// src/performance/dataloader.ts
import DataLoader from 'dataloader';
const workOrderLoader = new DataLoader<number, any>(
async (ids: readonly number[]) => {
// Batch: fetch multiple work orders in parallel
const results = await Promise.all(
ids.map((id) =>
client.get(`/workorders/${id}`).then((r) => r.data)
),
);
// Return in same order as input ids
return ids.map((id) => results.find((r) => r.id === id) || null);
},
{
maxBatchSize: 25,
cacheKeyFn: (id) => String(id),
},
);
// These 3 calls collapse into 1 batched operation:
const [wo1, wo2, wo3] = await Promise.all([
workOrderLoader.load(100),
workOrderLoader.load(200),
workOrderLoader.load(100), // deduped, same as first
]);
Step 4: Efficient Pagination
// Fetch only the fields you need (if API supports field selection)
// Use larger page sizes to reduce round trips
async function efficientFetchAll(client: any, endpoint: string, key: string) {
const all = [];
let cursor: string | undefined;
let pageCount = 0;
const startTime = Date.now();
do {
const { data } = await client.get(endpoint, {
params: { limit: 100, cursor }, // Max page size
});
all.push(...data[key]);
cursor = data.cursor;
pageCount++;
} while (cursor);
const elapsed = Date.now() - startTime;
console.log(`Fetched ${all.length} items in ${pageCount} pages (${elapsed}ms)`);
return all;
}
// Parallel pagination for independent resources
async function fetchAllResources(client: any) {
const [workOrders, assets, locations] = await Promise.all([
efficientFetchAll(client, '/workorders', 'workOrders'),
efficientFetchAll(client, '/assets', 'assets'),
efficientFetchAll(client, '/locations', 'locations'),
]);
return { workOrders, assets, locations };
}
Step 5: Request Deduplication
// src/performance/dedup.ts
class RequestDeduplicator {
private inflight = new Map<string, Promise<any>>();
async dedupe<T>(key: string, fetcher: () => Promise<T>): Promise<T> {
if (this.inflight.has(key)) {
return this.inflight.get(key)! as Promise<T>;
}
const promise = fetcher().finally(() => {
this.inflight.delete(key);
});
this.inflight.set(key, promise);
return promise;
}
}
const dedup = new RequestDeduplicator();
// 10 concurrent calls to getWorkOrder(123) = 1 actual API call
async function getWorkOrder(id: number) {
return dedup.dedupe(`wo:${id}`, () => client.get(`/workorders/${id}`));
}
Performance Benchmarks
| Optimization | Before | After | Improvement |
|---|---|---|---|
| Connection pooling | 350ms/req | 150ms/req | 57% faster |
| L1 cache (hot path) | 150ms/req | < 1ms/req | 99% faster |
| DataLoader batching | 10 calls | 1 call | 90% fewer requests |
| Max page size (100) | 50 pages | 10 pages | 5x fewer round trips |
| Request dedup | N calls | 1 call | (N-1) saved |
Output
- Connection pooling with keep-alive (reuses TCP connections)
- Multi-level cache (L1 in-memory + L2 Redis)
- DataLoader for batching and deduplication of entity fetches
- Efficient pagination with max page sizes
- Request deduplication preventing redundant concurrent calls
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Stale cache data | TTL too long | Reduce TTL, invalidate on writes |
| Memory growth | Unbounded cache | Set max size, use LRU eviction |
| DataLoader errors | One item in batch fails | Handle per-item errors in batch function |
| Connection pool exhaustion | Too many concurrent requests | Increase maxSockets or add queue |
Resources
- MaintainX API Reference
- DataLoader -- Batching and caching utility
- Node.js HTTP Agent
Next Steps
For cost optimization, see maintainx-cost-tuning.
Examples
Benchmark your API response times:
# Measure latency for 10 sequential requests
for i in $(seq 1 10); do
curl -s -o /dev/null -w "Request $i: %{time_total}s\n" \
"https://api.getmaintainx.com/v1/workorders?limit=1" \
-H "Authorization: Bearer $MAINTAINX_API_KEY"
done
When not to use it
- →When performance baseline measurements are unavailable
- →When using environments without Redis for shared caching
Prerequisites
Limitations
- →Stale cache data if TTL is too long
- →Memory growth from unbounded cache
- →Connection pool exhaustion under high concurrency
How it compares
Unlike standard sequential API calls, this approach reduces latency by reusing connections and collapsing multiple requests into batched operations.
Compared to similar skills
maintainx-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| maintainx-performance-tuning (this skill) | 0 | 26d | Caution | Intermediate |
| chrome-devtools | 41 | 7mo | Review | Intermediate |
| bullmq-specialist | 25 | 6mo | No flags | Intermediate |
| perf-lighthouse | 13 | 5mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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