instantly-performance-tuning
Techniques for optimizing Instantly.ai API throughput using caching, batching, and concurrent request management.
Install
mkdir -p .claude/skills/instantly-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/9209" && unzip -o skill.zip -d .claude/skills/instantly-performance-tuning && rm skill.zipInstalls to .claude/skills/instantly-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 Instantly.ai API performance with caching, batching, and connectionKey capabilities
- →Cache campaign analytics data
- →Batch lead operations with concurrency control
- →Implement cursor-based pagination
- →Manage connection reuse with Keep-Alive
- →Throttle email listing requests
How it works
This skill provides TypeScript patterns to minimize API load through caching and manages throughput using controlled concurrency and request throttling.
Inputs & outputs
When to use instantly-performance-tuning
- →Caching campaign analytics to reduce API load
- →Batching lead operations to increase throughput
- →Managing concurrent requests to avoid rate limits
- →Optimizing pagination for large datasets
About this skill
Instantly Performance Tuning
Overview
Optimize Instantly API v2 integrations for speed and throughput. Key areas: caching analytics data, batching lead operations, concurrent request management, efficient pagination, and connection reuse. The email listing endpoint has a strict 20 req/min limit that requires special handling.
Prerequisites
- Completed
instantly-install-authsetup - Working Instantly integration
- Understanding of async patterns and caching strategies
Instructions
Step 1: Cache Analytics Data
Campaign analytics don't change every second — cache them for 5-15 minutes to avoid redundant API calls.
class InstantlyCache {
private cache = new Map<string, { data: unknown; expiry: number }>();
get<T>(key: string): T | null {
const entry = this.cache.get(key);
if (!entry || Date.now() > entry.expiry) {
this.cache.delete(key);
return null;
}
return entry.data as T;
}
set(key: string, data: unknown, ttlMs: number) {
this.cache.set(key, { data, expiry: Date.now() + ttlMs });
}
}
const cache = new InstantlyCache();
async function getCachedAnalytics(campaignId: string) {
const cacheKey = `analytics:${campaignId}`;
const cached = cache.get<CampaignAnalytics>(cacheKey);
if (cached) return cached;
const data = await instantly<CampaignAnalytics>(
`/campaigns/analytics?id=${campaignId}`
);
cache.set(cacheKey, data, 5 * 60 * 1000); // 5 min TTL
return data;
}
// Cache campaign list (changes infrequently)
async function getCachedCampaigns() {
const cacheKey = "campaigns:all";
const cached = cache.get<Campaign[]>(cacheKey);
if (cached) return cached;
const campaigns = await instantly<Campaign[]>("/campaigns?limit=100");
cache.set(cacheKey, campaigns, 15 * 60 * 1000); // 15 min TTL
return campaigns;
}
Step 2: Batch Lead Operations with Controlled Concurrency
interface BatchResult<T> {
succeeded: T[];
failed: Array<{ input: unknown; error: string }>;
duration: number;
}
async function batchAddLeads(
campaignId: string,
leads: Array<{ email: string; first_name?: string; company_name?: string }>,
options = { concurrency: 5, delayMs: 200, retries: 3 }
): Promise<BatchResult<Lead>> {
const start = Date.now();
const succeeded: Lead[] = [];
const failed: Array<{ input: unknown; error: string }> = [];
let active = 0;
const addWithRetry = async (lead: typeof leads[0]) => {
for (let attempt = 0; attempt <= options.retries; attempt++) {
try {
const result = await instantly<Lead>("/leads", {
method: "POST",
body: JSON.stringify({
campaign: campaignId,
email: lead.email,
first_name: lead.first_name,
company_name: lead.company_name,
skip_if_in_workspace: true,
}),
});
succeeded.push(result);
return;
} catch (err: any) {
if (err.status === 429) {
await new Promise((r) => setTimeout(r, Math.pow(2, attempt) * 1000));
continue;
}
if (attempt === options.retries) {
failed.push({ input: lead, error: err.message });
}
}
}
};
// Process in chunks
for (let i = 0; i < leads.length; i += options.concurrency) {
const chunk = leads.slice(i, i + options.concurrency);
await Promise.allSettled(chunk.map(addWithRetry));
if (i + options.concurrency < leads.length) {
await new Promise((r) => setTimeout(r, options.delayMs));
}
// Progress report
const progress = Math.min(i + options.concurrency, leads.length);
console.log(`Progress: ${progress}/${leads.length} (${succeeded.length} ok, ${failed.length} failed)`);
}
return { succeeded, failed, duration: Date.now() - start };
}
Step 3: Efficient Pagination
// Pre-fetch next page while processing current page
async function* prefetchPaginate<T extends { id: string }>(
path: string,
pageSize = 100
): AsyncGenerator<T[]> {
let startingAfter: string | undefined;
let nextPagePromise: Promise<T[]> | null = null;
const fetchPage = (after?: string) => {
const qs = new URLSearchParams({ limit: String(pageSize) });
if (after) qs.set("starting_after", after);
return instantly<T[]>(`${path}?${qs}`);
};
// Fetch first page
let currentPage = await fetchPage();
while (currentPage.length > 0) {
// Start fetching next page immediately
if (currentPage.length === pageSize) {
const lastId = currentPage[currentPage.length - 1].id;
nextPagePromise = fetchPage(lastId);
} else {
nextPagePromise = null;
}
yield currentPage;
if (!nextPagePromise) break;
currentPage = await nextPagePromise;
}
}
// Usage — processes next page while current page is being handled
for await (const batch of prefetchPaginate<Lead>("/leads/list")) {
for (const lead of batch) {
// Process lead — next page is already loading
}
}
Step 4: Connection Reuse with Keep-Alive
import { Agent } from "undici";
// Create a persistent connection pool
const dispatcher = new Agent({
keepAliveTimeout: 30000, // keep connections alive for 30s
keepAliveMaxTimeout: 60000,
connections: 10, // max 10 concurrent connections
pipelining: 1,
});
async function instantlyPooled<T>(path: string, options: RequestInit = {}): Promise<T> {
const url = `https://api.instantly.ai/api/v2${path}`;
const res = await fetch(url, {
...options,
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${process.env.INSTANTLY_API_KEY}`,
...options.headers,
},
// @ts-ignore — undici dispatcher
dispatcher,
});
if (!res.ok) throw new Error(`Instantly ${res.status}: ${await res.text()}`);
return res.json() as Promise<T>;
}
Step 5: Throttled Email Fetcher (20 req/min limit)
class ThrottledEmailClient {
private timestamps: number[] = [];
private readonly maxPerMinute = 18; // leave margin
private async throttle() {
const now = Date.now();
this.timestamps = this.timestamps.filter((t) => now - t < 60000);
if (this.timestamps.length >= this.maxPerMinute) {
const wait = 60000 - (now - this.timestamps[0]) + 500;
await new Promise((r) => setTimeout(r, wait));
}
this.timestamps.push(Date.now());
}
async listEmails(params: { campaign_id?: string; limit?: number; starting_after?: string }) {
await this.throttle();
const qs = new URLSearchParams();
if (params.campaign_id) qs.set("campaign_id", params.campaign_id);
if (params.limit) qs.set("limit", String(params.limit));
if (params.starting_after) qs.set("starting_after", params.starting_after);
return instantly(`/emails?${qs}`);
}
async getUnreadCount() {
await this.throttle();
return instantly("/emails/unread/count");
}
}
Performance Benchmarks
| Operation | Unoptimized | Optimized | Improvement |
|---|---|---|---|
| 500 lead import | ~250s (sequential) | ~30s (5 concurrent + batch) | 8x |
| Campaign analytics (10 queries) | 10 API calls | 1 API call (cached) | 10x |
| All campaigns page load | ~2s (no cache) | ~50ms (cached) | 40x |
| Lead pagination (10K leads) | ~100s (sequential) | ~50s (prefetch) | 2x |
Error Handling
| Error | Cause | Solution |
|---|---|---|
429 during batch import | Too many concurrent requests | Reduce concurrency, increase delay |
429 on email listing | >20 req/min | Use ThrottledEmailClient |
| Stale cache data | TTL too long | Reduce TTL or add cache invalidation |
| Memory issues | Large pagination result set | Use async generators, process in chunks |
Resources
Next Steps
For cost optimization, see instantly-cost-tuning.
When not to use it
- →When cache TTL is too long for real-time data needs
- →When API rate limits are not a concern
Prerequisites
Limitations
- →Email listing endpoint has a strict 20 req/min limit
- →Stale cache data if TTL is not managed
How it compares
It replaces sequential, unmanaged API calls with optimized patterns like pre-fetching and rate-limit-aware batching.
Compared to similar skills
instantly-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| instantly-performance-tuning (this skill) | 0 | 27d | Caution | Intermediate |
| documenso-performance-tuning | 0 | 27d | Review | Intermediate |
| gamma-performance-tuning | 0 | 27d | No flags | Intermediate |
| juicebox-prod-checklist | 1 | 27d | Caution | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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