Claude-code-plugins salesloft-performance-tuning
install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/saas-packs/salesloft-pack/skills/salesloft-performance-tuning" ~/.claude/skills/jeremylongshore-claude-code-plugins-salesloft-performance-tuning && rm -rf "$T"
manifest:
plugins/saas-packs/salesloft-pack/skills/salesloft-performance-tuning/SKILL.mdsource content
SalesLoft Performance Tuning
Overview
Optimize SalesLoft REST API v2 performance. Key bottlenecks: deep pagination (cost multiplier), no batch endpoints, and per-minute rate limits. Solutions: caching, incremental sync, and pagination-aware request planning.
Latency Benchmarks
| Operation | Typical | With Caching |
|---|---|---|
| GET /me.json | 80ms | N/A (auth) |
| GET /people.json (page 1) | 120ms | 1ms (cached) |
| POST /people.json | 200ms | N/A (write) |
| GET /activities/emails.json | 150ms | 1ms (cached) |
| Full sync (10k people) | ~20min | ~5min (incremental) |
Instructions
Step 1: Response Caching
import { LRUCache } from 'lru-cache'; const cache = new LRUCache<string, any>({ max: 5000, ttl: 60_000 }); async function cachedGet<T>(endpoint: string, params?: Record<string, any>): Promise<T> { const key = `${endpoint}:${JSON.stringify(params || {})}`; const hit = cache.get(key); if (hit) return hit as T; const { data } = await api.get(endpoint, { params }); cache.set(key, data); return data; } // Cache people lookups (frequent during cadence enrollment) const person = await cachedGet('/people.json', { email_addresses: ['alex@co.com'] });
Step 2: Incremental Sync with updated_at
// Only fetch records changed since last sync async function incrementalSync(lastSyncTime: string) { const updated: any[] = []; let page = 1; while (true) { const { data } = await api.get('/people.json', { params: { updated_at: { gt: lastSyncTime }, // ISO 8601 per_page: 100, page, sort_by: 'updated_at', sort_direction: 'ASC', }, }); updated.push(...data.data); if (page >= data.metadata.paging.total_pages) break; page++; } return { updated, newSyncTime: new Date().toISOString() }; }
Step 3: Avoid Deep Pagination Cost
// Deep pages cost 3-30x. Instead of paginating all 25k records, // use updated_at filter to get incremental changes function shouldUseIncremental(totalCount: number): boolean { // If total records > 1000, incremental sync is more efficient // Full pagination of 250 pages = 910 cost points vs. // incremental of last 50 changes = 1 page = 1 point return totalCount > 1000; }
Step 4: Connection Pooling
import { Agent } from 'https'; const agent = new Agent({ keepAlive: true, maxSockets: 10, // Max concurrent connections maxFreeSockets: 5, // Keep idle connections alive timeout: 30_000, }); const api = axios.create({ baseURL: 'https://api.salesloft.com/v2', headers: { Authorization: `Bearer ${process.env.SALESLOFT_API_KEY}` }, httpsAgent: agent, });
Step 5: Parallel Safe Reads
// Parallelize independent reads (each costs 1 point) const [people, cadences, activities] = await Promise.all([ api.get('/people.json', { params: { per_page: 100 } }), api.get('/cadences.json', { params: { per_page: 50 } }), api.get('/activities/emails.json', { params: { per_page: 100 } }), ]); // 3 points total, ~120ms parallel vs ~360ms sequential
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Cache stampede | TTL expiry under load | Stale-while-revalidate pattern |
| Incremental misses | Clock skew | Use from last response, not local clock |
| Connection timeout | Pool exhausted | Increase or reduce concurrency |
| Rate limit on bulk | Too many parallel requests | Use with |
Resources
Next Steps
For cost optimization, see
salesloft-cost-tuning.