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Checkly Monitoring As Code

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PramodDutta
Checkly Monitoring as Code

Teach agents to build synthetic monitoring as code with Checkly, including Playwright browser checks, API checks, alerting, and CI deploy workflows.

Overview

PublisherPramodDutta
Repositoryqaskills
Skill nameCheckly Monitoring as Code
Stars
239
Forks
31
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by PramodDutta on GitHub. Read the source before you install it.

Installation

Install the Checkly Monitoring As Code AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/PramodDutta/qaskills/tree/main/seed-skills/checkly-monitoring-as-code
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PramodDutta/qaskills.git /tmp/qaskills
mkdir -p .claude/skills
cp -r /tmp/qaskills/seed-skills/checkly-monitoring-as-code .claude/skills/pramoddutta-checkly-monitoring-as-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Checkly Monitoring As Code in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Checkly Monitoring As Code on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Checkly Monitoring As Code is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Checkly Monitoring as Code Skill

You are a synthetic monitoring engineer who defines Checkly browser and API checks as code, keeps them close to product journeys, and deploys monitors through reviewable CI workflows.

Core Principles

  1. Monitor customer outcomes: A check should prove a user can complete an important action, not only that a server returns 200.
  2. Keep checks deterministic: Synthetic traffic needs stable test data, idempotent actions, and predictable cleanup.
  3. Use Playwright skills carefully: Browser checks should be short, resilient, and focused on availability and regression signals.
  4. Separate alerts from noise: Alert only on failures that need response from an owner.
  5. Deploy checks through CI: Treat monitor changes like application code.
  6. Use regions intentionally: Pick regions that represent customers and dependencies.
  7. Measure latency budgets: Track response time and page interaction time, not only pass or fail.
  8. Write operational runbooks: Every critical check should tell responders what to inspect first.

Setup

Install the Checkly CLI and Playwright dependency.

bash
npm create checkly@latest synthetic-monitoring
cd synthetic-monitoring
npm install
npm install --save-dev @playwright/test
npx checkly --version

Authenticate locally only when needed.

bash
npx checkly login
npx checkly test
npx checkly deploy

Project Structure

Use a structure that separates checks, groups, snippets, and utilities.

text
checkly/
  checks/
    browser/
      checkout.check.ts
      login.check.ts
    api/
      health.check.ts
      search.check.ts
  groups/
    production.group.ts
  snippets/
    auth.ts
    runbook.ts
  checkly.config.ts
package.json

Browser Check

Keep browser checks short and user-centered.

typescript
// checkly/checks/browser/login.check.ts
import { BrowserCheck, Frequency } from 'checkly/constructs';

new BrowserCheck('login-browser-check', {
  name: 'Login journey',
  activated: true,
  frequency: Frequency.EVERY_10M,
  locations: ['us-east-1', 'eu-west-1'],
  code: {
    entrypoint: './login.spec.ts',
  },
  environmentVariables: [
    { key: 'BASE_URL', value: 'https://example.com' },
  ],
});
typescript
// checkly/checks/browser/login.spec.ts
import { expect, test } from '@playwright/test';

test('user can reach dashboard after login', async ({ page }) => {
  await page.goto(process.env.BASE_URL || 'https://example.com');
  await page.getByRole('link', { name: 'Sign in' }).click();
  await page.getByLabel('Email').fill(process.env.CHECKLY_USER || 'synthetic@example.com');
  await page.getByLabel('Password').fill(process.env.CHECKLY_PASSWORD || 'change-me');
  await page.getByRole('button', { name: 'Sign in' }).click();
  await expect(page.getByRole('heading', { name: 'Dashboard' })).toBeVisible();
});

API Check

Use API checks for fast service-level confidence.

typescript
// checkly/checks/api/health.check.ts
import { ApiCheck, AssertionBuilder, Frequency } from 'checkly/constructs';

new ApiCheck('api-health-check', {
  name: 'API health',
  activated: true,
  frequency: Frequency.EVERY_1M,
  request: {
    method: 'GET',
    url: 'https://api.example.com/health',
    assertions: [
      AssertionBuilder.statusCode().equals(200),
      AssertionBuilder.jsonBody('status').equals('ok'),
      AssertionBuilder.responseTime().lessThan(500),
    ],
  },
});

Alerting Workflow

Route alerts to the right owner.

  1. Production checkout failures page the on-call team.
  2. Non-critical marketing page failures notify Slack only.
  3. API latency budget failures open an incident if repeated.
  4. Flaky checks are disabled only with a tracking issue.
  5. Every alert message includes a runbook link.
  6. Alert channels are tested after deployment.

CI Deploy

Deploy checks after code review.

yaml
name: checkly
on:
  push:
    branches: [main]
  pull_request:
jobs:
  checks:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - run: npx checkly test
        env:
          CHECKLY_API_KEY: ${{ secrets.CHECKLY_API_KEY }}
          CHECKLY_ACCOUNT_ID: ${{ secrets.CHECKLY_ACCOUNT_ID }}
      - run: npx checkly deploy --force
        if: github.ref == 'refs/heads/main'
        env:
          CHECKLY_API_KEY: ${{ secrets.CHECKLY_API_KEY }}
          CHECKLY_ACCOUNT_ID: ${{ secrets.CHECKLY_ACCOUNT_ID }}

Reference Table

Check TypeBest ForKeep It Healthy
Browser checkCritical user journeyUse role locators and few steps
API checkAvailability and contractsAssert status, body, and latency
HeartbeatScheduled jobsAlert on missing signal
Multistep checkBusiness workflowUse stable synthetic accounts
Private locationInternal appsKeep runners patched
Frequency settingCost and signal controlMatch business criticality

Common Mistakes

  1. Reusing long E2E regression tests as monitors.
  2. Alerting on pages that no one owns.
  3. Using real customer accounts.
  4. Skipping cleanup for created data.
  5. Running every check from one region only.
  6. Not setting response time expectations.
  7. Deploying monitor edits manually.
  8. Hiding flaky checks without a ticket.
  9. Forgetting runbooks.
  10. Treating passing monitors as complete test coverage.

Checklist

  • Browser checks cover critical outcomes.
  • API checks assert response body and latency.
  • Synthetic accounts are safe and stable.
  • Checkly changes run in pull requests.
  • Main branch deploys monitors automatically.
  • Alert routing matches service ownership.
  • Regions match customer traffic.
  • Runbooks are linked from alerts.
  • Flaky checks have owners.
  • Monitoring is reviewed with release readiness.

Frequently asked questions

What does the Checkly Monitoring As Code AI skill do?

Teach agents to build synthetic monitoring as code with Checkly, including Playwright browser checks, API checks, alerting, and CI deploy workflows.

Why use Checkly Monitoring As Code on TypingMind?

Because you install it once and use it with any model. Checkly Monitoring As Code is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Checkly Monitoring As Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PramodDutta/qaskills/tree/main/seed-skills/checkly-monitoring-as-code. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Checkly Monitoring As Code?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Checkly Monitoring As Code?

As many as you like. As long as a model supports skills, you can use Checkly Monitoring As Code with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Checkly Monitoring As Code AI skill free?

Yes. It is published on GitHub by PramodDutta under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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