customerio-rate-limits
Manage Customer.io API rate limits and implement robust retry logic.
Install
mkdir -p .claude/skills/customerio-rate-limits && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8362" && unzip -o skill.zip -d .claude/skills/customerio-rate-limits && rm skill.zipInstalls to .claude/skills/customerio-rate-limits
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.
Implement Customer.io rate limiting and backoff.Key capabilities
- →Implement token bucket rate limiters
- →Apply exponential backoff with jitter
- →Perform controlled bulk data imports
How it works
It provides TypeScript implementations for token bucket limiters and exponential backoff functions to manage API requests and respect the 100 req/sec limit.
Inputs & outputs
When to use customerio-rate-limits
- →Handle 429 errors
- →Implement backoff strategy
- →Throttle API requests
- →Optimize batch processing
About this skill
Customer.io Rate Limits
Overview
Understand Customer.io's API rate limits and implement proper throttling: token bucket limiters, exponential backoff with jitter, queue-based processing, and 429 response handling.
Rate Limit Reference
| API | Endpoint | Limit | Scope |
|---|---|---|---|
| Track API | identify, track, trackAnonymous | ~100 req/sec | Per workspace |
| Track API | Batch operations | ~100 req/sec | Per workspace |
| App API | Transactional email/push | ~100 req/sec | Per workspace |
| App API | Broadcasts, queries | ~10 req/sec | Per workspace |
These are approximate. Customer.io uses sliding window rate limiting. When exceeded, you get a 429 Too Many Requests response.
Instructions
Step 1: Token Bucket Rate Limiter
// lib/rate-limiter.ts
export class TokenBucket {
private tokens: number;
private lastRefill: number;
constructor(
private readonly maxTokens: number = 80, // Stay under 100/sec limit
private readonly refillRate: number = 80 // Tokens per second
) {
this.tokens = maxTokens;
this.lastRefill = Date.now();
}
private refill(): void {
const now = Date.now();
const elapsed = (now - this.lastRefill) / 1000;
this.tokens = Math.min(this.maxTokens, this.tokens + elapsed * this.refillRate);
this.lastRefill = now;
}
async acquire(): Promise<void> {
this.refill();
if (this.tokens >= 1) {
this.tokens -= 1;
return;
}
// Wait until a token is available
const waitMs = ((1 - this.tokens) / this.refillRate) * 1000;
await new Promise((r) => setTimeout(r, Math.ceil(waitMs)));
this.tokens = 0;
this.lastRefill = Date.now();
}
}
Step 2: Exponential Backoff with Jitter
// lib/backoff.ts
interface BackoffOptions {
maxRetries: number;
baseDelayMs: number;
maxDelayMs: number;
jitter: number; // 0 to 1
}
const DEFAULTS: BackoffOptions = {
maxRetries: 4,
baseDelayMs: 1000,
maxDelayMs: 60000,
jitter: 0.25,
};
export async function withBackoff<T>(
fn: () => Promise<T>,
opts: Partial<BackoffOptions> = {}
): Promise<T> {
const { maxRetries, baseDelayMs, maxDelayMs, jitter } = { ...DEFAULTS, ...opts };
let lastErr: Error | undefined;
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
return await fn();
} catch (err: any) {
lastErr = err;
const status = err.statusCode ?? err.status;
// Don't retry 4xx errors (except 429)
if (status >= 400 && status < 500 && status !== 429) throw err;
if (attempt === maxRetries) break;
// Check Retry-After header (429 responses)
const retryAfter = err.headers?.["retry-after"];
let delay: number;
if (retryAfter) {
delay = parseInt(retryAfter) * 1000;
} else {
delay = Math.min(baseDelayMs * Math.pow(2, attempt), maxDelayMs);
}
// Add jitter to prevent thundering herd
delay += delay * jitter * Math.random();
console.warn(`CIO retry ${attempt + 1}/${maxRetries} in ${Math.round(delay)}ms`);
await new Promise((r) => setTimeout(r, delay));
}
}
throw lastErr;
}
Step 3: Rate-Limited Client
// lib/customerio-rate-limited.ts
import { TrackClient, RegionUS } from "customerio-node";
import { TokenBucket } from "./rate-limiter";
import { withBackoff } from "./backoff";
export class RateLimitedCioClient {
private client: TrackClient;
private limiter: TokenBucket;
constructor(siteId: string, apiKey: string, ratePerSec: number = 80) {
this.client = new TrackClient(siteId, apiKey, { region: RegionUS });
this.limiter = new TokenBucket(ratePerSec, ratePerSec);
}
async identify(userId: string, attrs: Record<string, any>): Promise<void> {
await this.limiter.acquire();
return withBackoff(() => this.client.identify(userId, attrs));
}
async track(userId: string, event: { name: string; data?: any }): Promise<void> {
await this.limiter.acquire();
return withBackoff(() => this.client.track(userId, event));
}
async trackAnonymous(event: {
anonymous_id: string;
name: string;
data?: any;
}): Promise<void> {
await this.limiter.acquire();
return withBackoff(() => this.client.trackAnonymous(event));
}
async suppress(userId: string): Promise<void> {
await this.limiter.acquire();
return withBackoff(() => this.client.suppress(userId));
}
async destroy(userId: string): Promise<void> {
await this.limiter.acquire();
return withBackoff(() => this.client.destroy(userId));
}
}
Step 4: Queue-Based Processing with p-queue
For sustained high volume, use p-queue for cleaner concurrency control:
// lib/customerio-queued.ts
import PQueue from "p-queue";
import { TrackClient, RegionUS } from "customerio-node";
const cio = new TrackClient(
process.env.CUSTOMERIO_SITE_ID!,
process.env.CUSTOMERIO_TRACK_API_KEY!,
{ region: RegionUS }
);
// Process at most 80 requests per second with max 10 concurrent
const queue = new PQueue({
concurrency: 10,
interval: 1000,
intervalCap: 80,
});
// Queue operations instead of calling directly
export function queueIdentify(userId: string, attrs: Record<string, any>) {
return queue.add(() => cio.identify(userId, attrs));
}
export function queueTrack(userId: string, name: string, data?: any) {
return queue.add(() => cio.track(userId, { name, data }));
}
// Monitor queue health
setInterval(() => {
console.log(
`CIO queue: pending=${queue.pending} size=${queue.size}`
);
}, 10000);
Install: npm install p-queue
Step 5: Bulk Import Strategy
For large data imports (>10K users), avoid hitting rate limits with controlled batching:
// scripts/bulk-import.ts
import { RateLimitedCioClient } from "../lib/customerio-rate-limited";
async function bulkImport(users: { id: string; attrs: Record<string, any> }[]) {
const client = new RateLimitedCioClient(
process.env.CUSTOMERIO_SITE_ID!,
process.env.CUSTOMERIO_TRACK_API_KEY!,
50 // Conservative rate — 50/sec for imports
);
let processed = 0;
let errors = 0;
for (const user of users) {
try {
await client.identify(user.id, user.attrs);
processed++;
} catch (err: any) {
errors++;
console.error(`Failed user ${user.id}: ${err.message}`);
}
if (processed % 1000 === 0) {
console.log(`Progress: ${processed}/${users.length} (${errors} errors)`);
}
}
console.log(`Done: ${processed} processed, ${errors} errors`);
}
Error Handling
| Scenario | Strategy |
|---|---|
429 received | Respect Retry-After header, fall back to exponential backoff |
| Burst traffic spike | Token bucket absorbs burst, queue holds overflow |
| Sustained high volume | Use p-queue with interval limiting |
| Bulk import | Use conservative rate (50/sec) with progress logging |
| Downstream timeout | Don't count as rate limit — retry normally |
Resources
Next Steps
After implementing rate limits, proceed to customerio-security-basics for security best practices.
Prerequisites
Limitations
- →Requires implementation of custom rate-limiting logic
- →Limited to the documented 100 req/sec limit
How it compares
It provides ready-to-use TypeScript classes for rate limiting and backoff that are specifically designed for the Customer.io API.
Compared to similar skills
customerio-rate-limits side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| customerio-rate-limits (this skill) | 0 | 26d | Review | Intermediate |
| deepgram-performance-tuning | 3 | 26d | Review | Intermediate |
| graphql | 6 | 6mo | No flags | Advanced |
| guidewire-sdk-patterns | 2 | 26d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
deepgram-performance-tuning
jeremylongshore
Optimize Deepgram API performance for faster transcription and lower latency. Use when improving transcription speed, reducing latency, or optimizing audio processing pipelines. Trigger with phrases like "deepgram performance", "speed up deepgram", "optimize transcription", "deepgram latency", "deepgram faster".
graphql
davila7
GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
guidewire-sdk-patterns
jeremylongshore
Master Guidewire SDK patterns including Digital SDK, REST API Client, and Gosu best practices. Use when implementing integrations, building frontends with Jutro, or writing server-side Gosu code. Trigger with phrases like "guidewire sdk", "digital sdk", "jutro sdk", "guidewire patterns", "gosu best practices", "rest api client".
groq-performance-tuning
jeremylongshore
Optimize Groq API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Groq integrations. Trigger with phrases like "groq performance", "optimize groq", "groq latency", "groq caching", "groq slow", "groq batch".
openrouter-streaming-setup
jeremylongshore
Implement streaming responses with OpenRouter. Use when building real-time chat interfaces or reducing time-to-first-token. Trigger with phrases like 'openrouter streaming', 'openrouter sse', 'stream response', 'real-time openrouter'.
perplexity-multi-env-setup
jeremylongshore
Configure Perplexity across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Perplexity configurations. Trigger with phrases like "perplexity environments", "perplexity staging", "perplexity dev prod", "perplexity environment setup", "perplexity config by env".