Agent skill

Customerio Load Scale

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Implement Customer.io load testing and horizontal scaling. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedTesting & QA

Install Customerio Load Scale

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill customerio-load-scale -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace customerio-load-scale --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/customerio-load-scale .claude/skills/customerio-load-scale && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
customerio-load-scale
GitHub stars
2.8k
Token cost
~2.5k tokens
SKILL.md length
325 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Implement Customer.io load testing and horizontal scaling. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 4 steps: k6 Load Test Script → Queue-Based Architecture → Kubernetes HPA Autoscaling → …
  • Preparing for high traffic
  • SKILL.md covers Prerequisites, Output, Examples and Overview, plus 6 more sections
  • Calls npm; reaches track.customer.io; needs CUSTOMERIO_TRACK_API_KEY and API_KEY

What it does

Customerio Load Scale is an agent skill from jeremylongshore/tons-of-skills-marketplace. Implement Customer.io load testing and horizontal scaling. Use when preparing for high traffic, running load tests, or designing queue-based architectures for scale. Trigger: "customer.io load test", "customer.io scale", "customer.io high volume", "customer.io k6", "customer.io performance test".

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/implementation-guide.md`). Compatibility notes: Designed for Claude Code

It sits in Testing & QA, covering Load testing. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Preparing for high traffic
  • Running load tests
  • Designing queue-based architectures for scale

Example prompts

  • “customer.io load test”
  • “customer.io scale”
  • “customer.io high volume”
  • “/customerio-load-scale”

Requirements

  • Node.js
  • A credential in API_KEY
  • A credential in CUSTOMERIO_TRACK_API_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Bash(npx:*), Bash(kubectl:*), Glob, Grep

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. k6 Load Test Script
  2. Queue-Based Architecture
  3. Kubernetes HPA Autoscaling
  4. Batch Sender for Bulk Operations

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npm:*)
    • Bash(npx:*)
    • Bash(kubectl:*)
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • track.customer.io

    Also links to:

    • k6.io
    • npmjs.com
    • bullmq.io
    • kubernetes.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • CUSTOMERIO_TRACK_API_KEY
    • API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Customerio Load Scale loads about 2.5k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 325 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 325 words, ~2,536 tokens.

Download SKILL.mdSave it as .claude/skills/customerio-load-scale/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
customerio-load-scale
description
Implement Customer.io load testing and horizontal scaling. Use when preparing for high traffic, running load tests, or designing queue-based architectures for scale. Trigger: "customer.io load test", "customer.io scale", "customer.io high volume", "customer.io k6", "customer.io performance test".
allowed-tools
Read, Write, Edit, Bash(npm:*), Bash(npx:*), Bash(kubectl:*), Glob, Grep
compatibility
Designed for Claude Code
version
1.14.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, customer-io, load-testing, scaling, performance

Customer.io Load & Scale

Prerequisites

  • A baseline for event volume, queue depth, latency, error/rate-limit behavior, and an approved load window.
  • Synthetic payloads, capacity owner, delivery/consent guardrails, and a rollback decision threshold.

Output

  • A measured load/capacity result with bounded concurrency, rate-limit behavior, and owner-approved scale decision.
  • A rollback/recovery record that prevents duplicate or unauthorized customer messaging.

Examples

Run a staged load test using synthetic profiles and fixed idempotency keys, gradually increase only within the provider limit, and record throughput, 429s, queue age, and processing errors. Stop and reduce load on error/ordering regression; never use a live recipient list as a load-test fixture.

Overview

Load testing and scaling strategies for high-volume Customer.io integrations: k6 load test scripts, scaling architecture selection based on volume tier, Kubernetes HPA autoscaling, message queue buffering, and rate-limit-aware batch processing.

Scaling Architecture by Volume

Daily EventsArchitectureKey Components
< 100KDirect APISingleton client, retry, connection pooling
100K - 1MBatched APIEvent queue, batch processor, rate limiter
1M - 10MQueue-backedRedis/Kafka queue, worker pool, backpressure
> 10MDistributedMultiple workspaces, sharded queues, regional routing

Customer.io rate limit is ~100 req/sec per workspace. Plan your architecture around this.

Instructions

Step 1: k6 Load Test Script
javascript
// load-tests/customerio.js
// Run: k6 run --vus 10 --duration 60s load-tests/customerio.js
import http from "k6/http";
import { check, sleep } from "k6";
import { Counter, Trend } from "k6/metrics";

const SITE_ID = __ENV.CUSTOMERIO_SITE_ID;
const API_KEY = __ENV.CUSTOMERIO_TRACK_API_KEY;
const BASE_URL = "https://track.customer.io/api/v1";
const AUTH = `${SITE_ID}:${API_KEY}`;

const identifyLatency = new Trend("cio_identify_latency");
const trackLatency = new Trend("cio_track_latency");
const errors = new Counter("cio_errors");

export const options = {
  scenarios: {
    identify_load: {
      executor: "ramping-arrival-rate",
      startRate: 10,
      timeUnit: "1s",
      preAllocatedVUs: 20,
      maxVUs: 50,
      stages: [
        { duration: "30s", target: 50 },   // Ramp to 50/sec
        { duration: "60s", target: 80 },   // Hold at 80/sec (near limit)
        { duration: "30s", target: 10 },   // Cool down
      ],
    },
  },
  thresholds: {
    cio_identify_latency: ["p(95)<500", "p(99)<2000"],
    cio_track_latency: ["p(95)<500", "p(99)<2000"],
    cio_errors: ["count<50"],
  },
};

export default function () {
  const userId = `k6-load-${__VU}-${__ITER}`;
  const headers = {
    "Content-Type": "application/json",
    Authorization: `Basic ${encoding.b64encode(AUTH)}`,
  };

  // Identify
  const identifyRes = http.put(
    `${BASE_URL}/customers/${userId}`,
    JSON.stringify({
      email: `${userId}@loadtest.example.com`,
      _load_test: true,
      created_at: Math.floor(Date.now() / 1000),
    }),
    { headers }
  );

  identifyLatency.add(identifyRes.timings.duration);
  check(identifyRes, { "identify 200": (r) => r.status === 200 }) || errors.add(1);

  // Track event
  const trackRes = http.post(
    `${BASE_URL}/customers/${userId}/events`,
    JSON.stringify({
      name: "load_test_event",
      data: { iteration: __ITER, vu: __VU },
    }),
    { headers }
  );

  trackLatency.add(trackRes.timings.duration);
  check(trackRes, { "track 200": (r) => r.status === 200 }) || errors.add(1);

  sleep(0.1); // Small delay between iterations
}

// Cleanup function — suppress test users after test
export function teardown() {
  console.log("Load test complete. Clean up k6-load-* users in CIO dashboard.");
}

Run:

bash
k6 run --env CUSTOMERIO_SITE_ID="$CUSTOMERIO_SITE_ID" \
       --env CUSTOMERIO_TRACK_API_KEY="$CUSTOMERIO_TRACK_API_KEY" \
       load-tests/customerio.js
Step 2: Queue-Based Architecture
typescript
// services/cio-queue-worker.ts
import { Queue, Worker, QueueEvents } from "bullmq";
import { TrackClient, RegionUS } from "customerio-node";
import Bottleneck from "bottleneck";

const REDIS_URL = process.env.REDIS_URL ?? "redis://localhost:6379";

// Rate limiter: 80 requests per second (leave headroom under 100/sec limit)
const limiter = new Bottleneck({
  maxConcurrent: 15,
  reservoir: 80,
  reservoirRefreshAmount: 80,
  reservoirRefreshInterval: 1000,
});

const eventQueue = new Queue("cio:events", {
  connection: { url: REDIS_URL },
  defaultJobOptions: {
    attempts: 5,
    backoff: { type: "exponential", delay: 2000 },
    removeOnComplete: { count: 10000 },
    removeOnFail: { count: 50000 },
  },
});

// Producer — your application enqueues events here
export async function enqueueEvent(
  type: "identify" | "track",
  userId: string,
  data: Record<string, any>
): Promise<void> {
  await eventQueue.add(type, { userId, data, enqueuedAt: Date.now() });
}

// Consumer — workers process events with rate limiting
export function startEventWorkers(concurrency = 10): void {
  const cio = new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    { region: RegionUS }
  );

  const worker = new Worker(
    "cio:events",
    async (job) => {
      await limiter.schedule(async () => {
        if (job.name === "identify") {
          await cio.identify(job.data.userId, job.data.data);
        } else {
          await cio.track(job.data.userId, job.data.data);
        }
      });
    },
    {
      connection: { url: REDIS_URL },
      concurrency,
    }
  );

  worker.on("failed", (job, err) => {
    console.error(`CIO event failed: ${job?.id} — ${err.message}`);
  });

  // Monitor queue health
  const events = new QueueEvents("cio:events", {
    connection: { url: REDIS_URL },
  });

  setInterval(async () => {
    const counts = await eventQueue.getJobCounts();
    console.log(
      `CIO queue: waiting=${counts.waiting} active=${counts.active} ` +
      `failed=${counts.failed} completed=${counts.completed}`
    );
  }, 30000);
}
Step 3: Kubernetes HPA Autoscaling
yaml
# k8s/hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: cio-worker-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: cio-event-worker
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Pods
      pods:
        metric:
          name: cio_queue_depth
        target:
          type: AverageValue
          averageValue: "500"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
        - type: Pods
          value: 4
          periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
        - type: Pods
          value: 2
          periodSeconds: 120
Step 4: Batch Sender for Bulk Operations
typescript
// lib/cio-batch-sender.ts
import { TrackClient, RegionUS } from "customerio-node";
import Bottleneck from "bottleneck";

export async function batchSend(
  operations: Array<{
    type: "identify" | "track";
    userId: string;
    data: Record<string, any>;
  }>,
  ratePerSec = 80
): Promise<{ succeeded: number; failed: number }> {
  const cio = new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    { region: RegionUS }
  );

  const limiter = new Bottleneck({
    maxConcurrent: 15,
    reservoir: ratePerSec,
    reservoirRefreshAmount: ratePerSec,
    reservoirRefreshInterval: 1000,
  });

  let succeeded = 0;
  let failed = 0;

  const promises = operations.map((op, i) =>
    limiter.schedule(async () => {
      try {
        if (op.type === "identify") {
          await cio.identify(op.userId, op.data);
        } else {
          await cio.track(op.userId, op.data);
        }
        succeeded++;
      } catch {
        failed++;
      }
      if ((succeeded + failed) % 1000 === 0) {
        console.log(`Progress: ${succeeded + failed}/${operations.length}`);
      }
    })
  );

  await Promise.all(promises);
  return { succeeded, failed };
}

Install: npm install bottleneck bullmq

Load Test Checklist

  • Test against staging workspace (NEVER production)
  • Start at 10% of target rate, ramp up gradually
  • Monitor 429 error rate during test
  • Check Customer.io dashboard for processing lag
  • Verify cleanup of test users after load test
  • Document baseline latency and throughput numbers
  • Set up alerts before running at production scale

Error Handling

IssueSolution
429 during load testReduce rate, check limiter config
Queue backlog growingScale workers, increase concurrency
Memory pressureLimit batch and queue sizes, enable GC
k6 VU exhaustionIncrease preAllocatedVUs and maxVUs

Resources

Next Steps

After load testing, proceed to customerio-known-pitfalls for anti-patterns to avoid.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/customerio-load-scale of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation-guide.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Customerio Load Scale next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Customerio Load Scale compared with similar skills
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Customerio Load Scale this skilljeremylongshore/tons-of-skills-marketplace2.8k—~2.5kAutomated safety check: PassMIT
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Go Testingcxuu/golang-skills1731 repos~1.3kAutomated safety check: PassApache-2.0
Goalcraftgrp06/goalcraft102—~3.8kAutomated safety check: PassMIT
Thinking Partnermattnowdev/thinking-partner206—~4.4kAutomated safety check: PassMIT
Visionkunchenguid/vision331—~2.9kAutomated safety check: PassMIT

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Categories

Questions about Customerio Load Scale

What does Customerio Load Scale do?

Implement Customer.io load testing and horizontal scaling. An agent skill from jeremylongshore/tons-of-skills-marketplace. Customerio Load Scale is an agent skill from jeremylongshore/tons-of-skills-marketplace.io load testing and horizontal scaling.

When should I use Customerio Load Scale?

Customerio Load Scale fits situations like: preparing for high traffic; running load tests; designing queue-based architectures for scale.

How do I install Customerio Load Scale in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill customerio-load-scale -a claude-code`. Or copy the skill folder (skills/.curated/customerio-load-scale in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/customerio-load-scale in your project. Claude Code loads it when a task matches its description.

How do I install Customerio Load Scale in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill customerio-load-scale -a codex`. Or copy the skill folder (skills/.curated/customerio-load-scale in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/customerio-load-scale in your project. Codex loads it when a task matches its description.

Can I use Customerio Load Scale in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill customerio-load-scale -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customerio-load-scale, .gemini/skills/customerio-load-scale, .github/skills/customerio-load-scale and .opencode/skills/customerio-load-scale in your project.

What does Customerio Load Scale need to run?

Going by SKILL.md and its folder, Customerio Load Scale needs the command-line tools its instructions call (npm) and credentials named CUSTOMERIO_TRACK_API_KEY and API_KEY. Our summary lists: Node.js; A credential in API_KEY; A credential in CUSTOMERIO_TRACK_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Bash(npx:*), Bash(kubectl:*), Glob, Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Customerio Load Scale access the network?

SKILL.md names 5 domains. In commands or code: track.customer.io; the agent is likely to contact it when it follows the instructions. As links in the text: k6.io, npmjs.com, bullmq.io and kubernetes.io. This is read from the text; nothing was executed.

Is Customerio Load Scale safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Customerio Load Scale use?

Customerio Load Scale is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Customerio Load Scale use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.

What are the alternatives to Customerio Load Scale?

Skills that share tags, products or a category with Customerio Load Scale: Writing Livekit Scenarios (livekit-examples/agent-starter-python, 264 stars), Go Testing (cxuu/golang-skills, 173 stars), Goalcraft (grp06/goalcraft, 102 stars) and Thinking Partner (mattnowdev/thinking-partner, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customerio Load Scale?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.