Agent skill

Customerio Observability

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

Set up Customer.io monitoring and observability. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedDevOps & Cloud

Install Customerio Observability

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

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace customerio-observability --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-observability .claude/skills/customerio-observability && 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-observability
GitHub stars
2.8k
Token cost
~2.8k tokens
SKILL.md length
290 words
Files
3 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Set up Customer.io monitoring and observability. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 7 steps: Prometheus Metrics → Instrumented Client → Structured Logging with PII Redaction → …
  • Implementing metrics
  • SKILL.md covers Output, Examples, Overview and Prerequisites, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Customerio Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace. Set up Customer.io monitoring and observability. Use when implementing metrics, structured logging, alerting, or Grafana dashboards for Customer.io integrations. Trigger: "customer.io monitoring", "customer.io metrics", "customer.io dashboard", "customer.io alerts", "customer.io observability".

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/implementation-guide.md` and `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in DevOps & Cloud, covering Observability and Monitoring and alerting. It works with Grafana and Prometheus. 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

  • Implementing metrics
  • Structured logging
  • Grafana dashboards for Customer.io integrations

Example prompts

  • “customer.io monitoring”
  • “customer.io metrics”
  • “customer.io dashboard”
  • “/customerio-observability”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Bash(npx:*), Glob, Grep

Workflow steps

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

  1. Prometheus Metrics
  2. Instrumented Client
  3. Structured Logging with PII Redaction
  4. Webhook Metrics Collection
  5. Prometheus Metrics Endpoint
  6. Grafana Dashboard (JSON Model)
  7. Alerting Rules

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:*)
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript, json and yaml).

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

  • Network

    Links to these hosts (documentation or services it may open):

    • prometheus.io
    • grafana.com
    • getpino.io

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    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 Observability loads about 2.8k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 290 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
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.1k

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). 290 words, ~2,812 tokens.

Download SKILL.mdSave it as .claude/skills/customerio-observability/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
customerio-observability
description
Set up Customer.io monitoring and observability. Use when implementing metrics, structured logging, alerting, or Grafana dashboards for Customer.io integrations. Trigger: "customer.io monitoring", "customer.io metrics", "customer.io dashboard", "customer.io alerts", "customer.io observability".
allowed-tools
Read, Write, Edit, Bash(npm:*), Bash(npx:*), Glob, Grep
compatibility
Designed for Claude Code
version
1.14.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, customer-io, monitoring, observability, prometheus

Customer.io Observability

Output

  • Bounded metrics and redacted traces for event acceptance, campaign delivery, errors, latency, and rate-limit headroom.
  • An owned alert/runbook path for delivery, authentication, data-quality, and provider incidents.

Examples

Emit an aggregate counter for accepted events and failures by environment and endpoint, never by email address, customer ID, message body, API key, or full payload. Trigger a staging alert with a harmless test event, verify the on-call route, then restore the normal state and record the alert receipt.

Overview

Implement comprehensive observability for Customer.io integrations: Prometheus metrics (latency, error rates, delivery funnel), structured JSON logging with PII redaction, OpenTelemetry tracing, and Grafana dashboard definitions.

Prerequisites

  • Customer.io integration deployed
  • Prometheus + Grafana (or compatible metrics stack)
  • Structured logging system (pino recommended)

Key Metrics to Track

MetricTypeDescriptionAlert Threshold
cio_api_duration_msHistogramAPI call latencyp99 > 5000ms
cio_api_requests_totalCounterTotal API requests by operationN/A (rate)
cio_api_errors_totalCounterAPI errors by status code> 1% error rate
cio_email_sent_totalCounterTransactional + campaign emailsN/A
cio_email_bounced_totalCounterBounce count> 5% of sends
cio_email_complained_totalCounterSpam complaints> 0.1% of sends
cio_webhook_received_totalCounterWebhook events by metric typeN/A
cio_queue_depthGaugePending items in event queue> 10K

Instructions

Step 1: Prometheus Metrics
typescript
// lib/customerio-metrics.ts
import { Counter, Histogram, Gauge, Registry } from "prom-client";

const registry = new Registry();

export const cioMetrics = {
  apiDuration: new Histogram({
    name: "cio_api_duration_ms",
    help: "Customer.io API call duration in milliseconds",
    labelNames: ["operation", "status"] as const,
    buckets: [10, 25, 50, 100, 250, 500, 1000, 2500, 5000],
    registers: [registry],
  }),

  apiRequests: new Counter({
    name: "cio_api_requests_total",
    help: "Total Customer.io API requests",
    labelNames: ["operation"] as const,
    registers: [registry],
  }),

  apiErrors: new Counter({
    name: "cio_api_errors_total",
    help: "Customer.io API errors",
    labelNames: ["operation", "status_code"] as const,
    registers: [registry],
  }),

  emailSent: new Counter({
    name: "cio_email_sent_total",
    help: "Emails sent via Customer.io",
    labelNames: ["type"] as const,  // "transactional" or "campaign"
    registers: [registry],
  }),

  emailBounced: new Counter({
    name: "cio_email_bounced_total",
    help: "Email bounces from Customer.io webhooks",
    registers: [registry],
  }),

  emailComplained: new Counter({
    name: "cio_email_complained_total",
    help: "Spam complaints from Customer.io webhooks",
    registers: [registry],
  }),

  webhookReceived: new Counter({
    name: "cio_webhook_received_total",
    help: "Webhook events received",
    labelNames: ["metric"] as const,
    registers: [registry],
  }),

  queueDepth: new Gauge({
    name: "cio_queue_depth",
    help: "Pending items in Customer.io event queue",
    labelNames: ["queue"] as const,
    registers: [registry],
  }),
};

export { registry };
Step 2: Instrumented Client
typescript
// lib/customerio-instrumented.ts
import { TrackClient, APIClient, SendEmailRequest, RegionUS } from "customerio-node";
import { cioMetrics } from "./customerio-metrics";

export class InstrumentedCioClient {
  private track: TrackClient;
  private app: APIClient;

  constructor(siteId: string, trackKey: string, appKey: string) {
    this.track = new TrackClient(siteId, trackKey, { region: RegionUS });
    this.app = new APIClient(appKey, { region: RegionUS });
  }

  async identify(userId: string, attrs: Record<string, any>): Promise<void> {
    const timer = cioMetrics.apiDuration.startTimer({ operation: "identify" });
    cioMetrics.apiRequests.inc({ operation: "identify" });

    try {
      await this.track.identify(userId, attrs);
      timer({ status: "success" });
    } catch (err: any) {
      const code = String(err.statusCode ?? "unknown");
      timer({ status: "error" });
      cioMetrics.apiErrors.inc({ operation: "identify", status_code: code });
      throw err;
    }
  }

  async trackEvent(
    userId: string,
    name: string,
    data?: Record<string, any>
  ): Promise<void> {
    const timer = cioMetrics.apiDuration.startTimer({ operation: "track" });
    cioMetrics.apiRequests.inc({ operation: "track" });

    try {
      await this.track.track(userId, { name, data });
      timer({ status: "success" });
    } catch (err: any) {
      timer({ status: "error" });
      cioMetrics.apiErrors.inc({
        operation: "track",
        status_code: String(err.statusCode ?? "unknown"),
      });
      throw err;
    }
  }

  async sendEmail(request: SendEmailRequest): Promise<any> {
    const timer = cioMetrics.apiDuration.startTimer({ operation: "send_email" });
    cioMetrics.apiRequests.inc({ operation: "send_email" });

    try {
      const result = await this.app.sendEmail(request);
      timer({ status: "success" });
      cioMetrics.emailSent.inc({ type: "transactional" });
      return result;
    } catch (err: any) {
      timer({ status: "error" });
      cioMetrics.apiErrors.inc({
        operation: "send_email",
        status_code: String(err.statusCode ?? "unknown"),
      });
      throw err;
    }
  }
}
Step 3: Structured Logging with PII Redaction
typescript
// lib/customerio-logger.ts
import pino from "pino";

const logger = pino({
  name: "customerio",
  level: process.env.CUSTOMERIO_LOG_LEVEL ?? "info",
  redact: {
    paths: [
      "*.email",
      "*.phone",
      "*.ip_address",
      "*.password",
      "attrs.email",
      "attrs.phone",
    ],
    censor: "[REDACTED]",
  },
});

export function logCioOperation(
  operation: string,
  data: {
    userId?: string;
    event?: string;
    latencyMs?: number;
    statusCode?: number;
    error?: string;
    attrs?: Record<string, any>;
  }
): void {
  if (data.error) {
    logger.error({ operation, ...data }, `CIO ${operation} failed`);
  } else {
    logger.info({ operation, ...data }, `CIO ${operation} completed`);
  }
}

// Usage:
// logCioOperation("identify", {
//   userId: "user-123",
//   latencyMs: 85,
//   attrs: { email: "user@example.com", plan: "pro" }
// });
// Output: {"level":"info","operation":"identify","userId":"user-123",
//          "latencyMs":85,"attrs":{"email":"[REDACTED]","plan":"pro"},
//          "msg":"CIO identify completed"}
Step 4: Webhook Metrics Collection
typescript
// Integrate with webhook handler (see customerio-webhooks-events skill)
function recordWebhookMetrics(event: { metric: string }): void {
  cioMetrics.webhookReceived.inc({ metric: event.metric });

  switch (event.metric) {
    case "bounced":
      cioMetrics.emailBounced.inc();
      break;
    case "spammed":
      cioMetrics.emailComplained.inc();
      break;
    case "sent":
      cioMetrics.emailSent.inc({ type: "campaign" });
      break;
  }
}
Step 5: Prometheus Metrics Endpoint
typescript
// routes/metrics.ts
import { Router } from "express";
import { registry } from "../lib/customerio-metrics";

const router = Router();

router.get("/metrics", async (_req, res) => {
  res.set("Content-Type", registry.contentType);
  res.end(await registry.metrics());
});

export default router;
Step 6: Grafana Dashboard (JSON Model)
json
{
  "title": "Customer.io Integration",
  "panels": [
    {
      "title": "API Latency (p50/p95/p99)",
      "type": "timeseries",
      "targets": [
        { "expr": "histogram_quantile(0.50, rate(cio_api_duration_ms_bucket[5m]))" },
        { "expr": "histogram_quantile(0.95, rate(cio_api_duration_ms_bucket[5m]))" },
        { "expr": "histogram_quantile(0.99, rate(cio_api_duration_ms_bucket[5m]))" }
      ]
    },
    {
      "title": "Request Rate by Operation",
      "type": "timeseries",
      "targets": [
        { "expr": "rate(cio_api_requests_total[5m])" }
      ]
    },
    {
      "title": "Error Rate %",
      "type": "stat",
      "targets": [
        { "expr": "rate(cio_api_errors_total[5m]) / rate(cio_api_requests_total[5m]) * 100" }
      ]
    },
    {
      "title": "Email Delivery Funnel",
      "type": "bargauge",
      "targets": [
        { "expr": "cio_email_sent_total" },
        { "expr": "cio_email_bounced_total" },
        { "expr": "cio_email_complained_total" }
      ]
    }
  ]
}
Step 7: Alerting Rules
yaml
# prometheus/customerio-alerts.yml
groups:
  - name: customerio
    rules:
      - alert: CioHighErrorRate
        expr: rate(cio_api_errors_total[5m]) / rate(cio_api_requests_total[5m]) > 0.05
        for: 5m
        labels: { severity: critical }
        annotations:
          summary: "Customer.io API error rate > 5%"

      - alert: CioHighLatency
        expr: histogram_quantile(0.99, rate(cio_api_duration_ms_bucket[5m])) > 5000
        for: 5m
        labels: { severity: warning }
        annotations:
          summary: "Customer.io p99 latency > 5 seconds"

      - alert: CioHighBounceRate
        expr: rate(cio_email_bounced_total[1h]) / rate(cio_email_sent_total[1h]) > 0.05
        for: 15m
        labels: { severity: warning }
        annotations:
          summary: "Email bounce rate > 5%"

      - alert: CioSpamComplaints
        expr: rate(cio_email_complained_total[1h]) / rate(cio_email_sent_total[1h]) > 0.001
        for: 5m
        labels: { severity: critical }
        annotations:
          summary: "Spam complaint rate > 0.1% — sender reputation at risk"

Error Handling

IssueSolution
High cardinality metricsDon't use userId as a label — use operation + status only
Log volume too highSet CUSTOMERIO_LOG_LEVEL=warn in production
Missing metricsCheck metric registration and scrape config
PII in logsVerify pino redact paths cover all sensitive fields

Resources

Next Steps

After observability setup, proceed to customerio-advanced-troubleshooting for debugging.

© 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 2 other files (references) in skills/.curated/customerio-observability of jeremylongshore/tons-of-skills-marketplace.

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

Open the folder on GitHubat commit cfae287

Compare with similar skills

Customerio Observability 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.

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Frontmcp Observabilityagentfront/frontmcp146—~4.6kAutomated safety check: PassApache-2.0
Monitoring Observabilityahmedasmar/devops-claude-skills203—~3.9kAutomated safety check: PassNone
Monitoring ExpertJeffallan/claude-skills12k—~1.6kAutomated safety check: PassMIT

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Categories

Questions about Customerio Observability

What does Customerio Observability do?

Set up Customer.io monitoring and observability. An agent skill from jeremylongshore/tons-of-skills-marketplace. Customerio Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace.io monitoring and observability.

When should I use Customerio Observability?

Customerio Observability fits situations like: implementing metrics; structured logging; grafana dashboards for Customer.io integrations.

How do I install Customerio Observability in Claude Code?

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

How do I install Customerio Observability in Codex?

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

Can I use Customerio Observability 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-observability -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-observability, .gemini/skills/customerio-observability, .github/skills/customerio-observability and .opencode/skills/customerio-observability in your project.

What does Customerio Observability need to run?

SKILL.md names no scripts, command-line tools or credentials: Customerio Observability is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Bash(npx:*), Glob, Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Customerio Observability access the network?

SKILL.md names 3 domains. As links in the text: prometheus.io, grafana.com and getpino.io. This is read from the text; nothing was executed.

Is Customerio Observability 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 Observability use?

Customerio Observability 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 Observability use?

About 2.8k tokens (SKILL.md is roughly 11k 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 3.2k tokens, read only when the agent opens those files.

What are the alternatives to Customerio Observability?

Skills that share tags, products or a category with Customerio Observability: Happy Infra Metrics and Grafana (slopus/happy, 24k stars), Archestra Dev Observability (archestra-ai/archestra, 4.4k stars), Frontmcp Observability (agentfront/frontmcp, 146 stars) and Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customerio Observability?

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.