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Merge pull request #177 from checkly/document-agent-skill-1
Restructure AI integrations docs with Skills and Rules pages
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‎cli/overview.mdx‎

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import MainCicdCards from "/snippets/main-cicd-cards.mdx"
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The Checkly CLI gives you a JavaScript/TypeScript-native workflow for coding, testing and deploying synthetic
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monitoring at scale, from your code base. The Checkly CLI comes with **native `@playwright/test` support.** No lock-in,
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monitoring at scale, from your code base. The Checkly CLI comes with **native `@playwright/test` support.** No lock-in,
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just write standard `*.spec.ts` files.
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## Starting your first project
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npx checkly test
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```
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This should report the following output to your terminal
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This should report the following output to your terminal
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```
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Running 4 checks in eu-west-1.
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npx checkly deploy
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```
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You just created your entire synthetic monitoring setup with API and Playwright-based Browser Checks from your code base!
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You just created your entire synthetic monitoring setup with API and Playwright-based Browser Checks from your code base!
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Open up [your Checkly dashboard](https://app.checklyhq.com) and you should see your check, ready to start monitoring around the clock.
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For a custom installation check out [our installation docs](/cli/installation/).
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## Using AI IDEs and Copilots
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## Using Agents, AI IDEs and Copilots
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Checkly is designed to work with AI IDEs and Copilots. You can use your preferred provider to generate code for API
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Checkly is designed to work with Agents, AI IDEs, and Copilots. You can use your preferred provider to generate code for API
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Checks, Browser Checks and all other constructs.
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<Columns cols={3}>
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<Card title="GitHub Copilot" icon="/images/github-copilot-icon.svg" href="/integrations/ai/copilot">
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Download the Checkly instructions file and let copilot generate Checkly CLI code for you.
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<Columns cols={2}>
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<Card title="Checkly Skills" href="/integrations/ai/skills">
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Install Checkly Skills and let AI agents generate Checkly monitoring code for you.
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</Card>
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<Card title="Cursor" icon="/images/cursor-icon.svg" href="/integrations/ai/cursor">
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Download the Checkly `.mdc` rules file and let Cursor generate Checkly CLI code for you.
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</Card>
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<Card title="Claude Code" icon="/images/claude-icon.svg" href="/integrations/ai/claude">
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Download the Checkly rules file and let Claude generate Checkly CLI code for you.
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<Card title="Checkly Rules" href="/integrations/ai/rules">
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Include the Checkly Rules in your conversations to generate Checkly monitoring code for you.
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</Card>
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</Columns>
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After kicking the tires, you should delegate the testing and deploying of your checks to your CI/CD pipeline. Check our
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docs on [setting up the Checkly CLI with your favourite CI/CD platform](/integrations/ci-cd/overview).
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<MainCicdCards />
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<MainCicdCards />

‎docs.json‎

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"group": "Integrations",
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"pages": [
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{
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"group": "AI coding asssistants",
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"group": "AI Agents & Coding Assistants",
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"pages": [
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"integrations/ai/overview",
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"integrations/ai/claude",
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"integrations/ai/cursor",
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"integrations/ai/copilot",
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"integrations/ai/windsurf"
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"integrations/ai/skills",
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"integrations/ai/rules"
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]
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},
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{

‎integrations/ai/claude.mdx‎

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‎integrations/ai/copilot.mdx‎

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‎integrations/ai/cursor.mdx‎

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‎integrations/ai/overview.mdx‎

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keywords: ['mcp', 'ai', 'rules', 'generate checks', 'agents', 'context']
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---
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import { YoutubeEmbed } from "/snippets/youtube-embed.jsx"
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import AgentExamples from "/snippets/agent-example.mdx"
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From the beginning, Checkly has bet on [Monitoring as Code](/concepts/monitoring-as-code) which lets you create and control your monitoring infrastructure entirely using code. [Checkly constructs](/constructs/overview) can reflect all your monitoring properties.
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From the beginning, Checkly has bet on [Monitoring as Code](/concepts/monitoring-as-code) which lets you create and control your monitoring infrastructure entirely using code.
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By default, [Checkly constructs](/constructs/overview) reflect all your monitoring properties.
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```ts api.check.ts
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import { ApiCheck, AssertionBuilder } from "checkly/constructs"
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**The Monitoring as Code workflow is by default AI-native** because LLMs are excellent at writing and editing Checkly constructs code and modern AI agents can execute CLI commands easily.
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You only need to provide the necessary context about Checkly and your monitoring setup to your AI agent of choice.
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## Add custom Checkly rules to your AI conversation
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Provide the necessary Checkly context and let your AI agent of choice do the rest.
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The [`checkly.rules.md` file](https://www.checklyhq.com/docs/ai/checkly.rules.md) includes best practices, example code and required CLI commands to give your AI workflow enough context to perform Checkly-related tasks.
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<AgentExamples />
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Once the Checkly rules are included in your AI context window, your agent can effectively assist you in managing your monitoring setup.
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## Add Checkly context to your AI agent conversation
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It will be able to:
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<Card title="Create new checks, alert channels or other constructs" horizontal>
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"Can you create a new `BrowserCheck` monitoring `example.com`"
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</Card>
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Install [Checkly Skills](/integrations/ai/skills) or add the [Checkly Rules](/integrations/ai/rules) to your AI conversation to give your AI agent enough context to perform Checkly-related tasks.
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<Card title="Gather information about the current monitoring setup" horizontal>
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"What are the currently used monitoring locations?"
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<Columns cols={2}>
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<Card title="Checkly Skills" href="/integrations/ai/skills" cta="Get started">
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Let your agents pull in all required Checkly context on demand.
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</Card>
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<Card title="Bulk-update your monitoring resources" horizontal>
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"Can you change all checks to run every 5 minutes instead of every 10 minutes?"
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<Card title="Checkly Rules" href="/integrations/ai/rules" cta="Get started">
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Include the entire Checkly context in commands or documentation.
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</Card>
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</Columns>
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With enough application context you can even create checks for your specific use cases.
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### Skills vs Rules
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<Card title="Analyze application code and create the monitoring setup" horizontal>
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"Can you create new API Checks for the application API endpoints?"
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</Card>
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Find a live session explaining how to automate Checkly monitoring with AI below and [read the "Agentic Workflows" guide](/guides/agentic-workflows) for more details.
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**Use Skills** when your AI agent supports the [Agent Skills](https://agentskills.io) standard. Skills load context on demand, keeping your agent's context window lean until Checkly-related tasks arise. This is the recommended approach for compatible agents.
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<YoutubeEmbed id="WqTXa7GCk-k" title="No Coding! Just Prompting! Getting the most out of AI for Application Reliability." />
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**Use Rules** when your agent doesn't support skills or when you want the Checkly context always available. Rules files are loaded at session start and provide consistent context throughout your conversation.
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## Why is there no Checkly MCP server?
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## Why is there no Checkly MCP server (yet)?
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The MCP concept is often used to enable LLMs to interact with external systems. It acts as a bridge between the AI model and the target system, translating natural language commands into actionable API calls or code snippets.
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**With Monitoring as Code, Checkly already provides a native way to control your monitoring infrastructure via code and the command line.**
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Whether you need to create new resources or update existing ones, AI can write and update the necessary construct files and execute the Checkly CLI commands autonomously.
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## Additional Resources
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Follow these guides if you use one of the popular AI coding tools:
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- [Claude Code](/integrations/ai/claude)
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- [Cursor](/integrations/ai/cursor)
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- [GitHub Copilot](/integrations/ai/copilot)
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<Tip>
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We are researching additional AI-native workflows. [Let us know in the public roadmap](https://feedback.checklyhq.com/p/checkly-mcp) if you are interested in more agent-friendly integrations.
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</Tip>

‎integrations/ai/rules.mdx‎

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---
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title: 'Checkly Rules'
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description: 'Use the provided Checkly rules to control, update and deploy your monitoring.'
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sidebarTitle: 'Checkly Rules'
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---
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import AgentExamples from "/snippets/agent-example.mdx"
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import { YoutubeEmbed } from "/snippets/youtube-embed.jsx"
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The [`checkly.rules.md` file](https://www.checklyhq.com/docs/ai/checkly.rules.md) includes best practices, example code and required CLI commands to give your AI workflow enough context to perform Checkly-related tasks.
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Once the Checkly rules are included in your AI context window, your agent can effectively assist you in managing your monitoring setup.
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It will be able to:
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<AgentExamples />
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With enough application context, you can even create checks for your specific use cases.
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<Card title="Analyze application code and create the monitoring setup" horizontal>
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"Can you create new API Checks for the application API endpoints?"
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</Card>
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Find a live session explaining how to automate Checkly monitoring with AI below and [read the "Agentic Workflows" guide](/guides/agentic-workflows) for more details.
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<YoutubeEmbed id="WqTXa7GCk-k" title="No Coding! Just Prompting! Getting the most out of AI for Application Reliability." />
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## Claude Code
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Claude Code reads instructions from `CLAUDE.md` files. You can place these files globally (in your home directory) or locally (in your project root). Claude Code automatically includes these files in its context.
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To use Checkly rules with Claude Code, download the rules file and reference it in your `CLAUDE.md`:
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<Tabs>
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<Tab title="Mac and Linux">
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```bash
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mkdir -p .claude &&
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curl -o .claude/checkly.rules.md https://www.checklyhq.com/docs/ai/checkly.rules.md -L
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echo "- examine checkly.rules.md for code generation rules" >> .claude/CLAUDE.md
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```
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</Tab>
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<Tab title="Windows">
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```powershell
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New-Item -ItemType Directory -Path ".claude" -Force
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Invoke-WebRequest -Uri "https://www.checklyhq.com/docs/ai/checkly.rules.md" -OutFile ".claude\checkly.rules.md"
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Add-Content -Path ".claude\CLAUDE.md" -Value "- examine checkly.rules.md for code generation rules"
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```
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</Tab>
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</Tabs>
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Restart your Claude Code session to load the instructions.
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## GitHub Copilot
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GitHub Copilot reads project-level instructions from `.github/copilot-instructions.md`. This file is automatically included in Copilot's context for all chat interactions.
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<Tabs>
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<Tab title="Mac and Linux">
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```bash
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mkdir -p .github && curl -o .github/copilot-instructions.md "https://www.checklyhq.com/docs/ai/checkly.rules.md" -L
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```
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</Tab>
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<Tab title="Windows">
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```powershell
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New-Item -ItemType Directory -Path ".github" -Force
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Invoke-WebRequest -Uri "https://www.checklyhq.com/docs/ai/checkly.rules.md" -OutFile ".github\copilot-instructions.md"
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```
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</Tabs>
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## Cursor
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Cursor uses `.mdc` (Markdown Cursor) files stored in `.cursor/rules/` for project-specific instructions. These rules are automatically included in Cursor's context.
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<Tabs>
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<Tab title="Mac and Linux">
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```bash
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mkdir -p .cursor/rules && curl -o .cursor/rules/checkly.mdc "https://www.checklyhq.com/docs/ai/checkly.rules.md" -L
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```
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<Tab title="Windows">
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```powershell
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Invoke-WebRequest -Uri "https://www.checklyhq.com/docs/ai/checkly.rules.md" -OutFile ".cursor\rules\checkly.mdc"
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```
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</Tabs>
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You can reference the rules file explicitly using `@checkly.mdc` in your Cursor chats.
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## Windsurf
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Windsurf stores rules in `.windsurf/rules/` as Markdown files. These are included in the AI context when you interact with Windsurf's assistant.
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<Tabs>
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<Tab title="Mac and Linux">
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```bash
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mkdir -p .windsurf/rules && curl -o .windsurf/rules/checkly.md "https://www.checklyhq.com/docs/ai/checkly.rules.md" -L
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```
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<Tab title="Windows">
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New-Item -ItemType Directory -Path ".windsurf\rules" -Force
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Invoke-WebRequest -Uri "https://www.checklyhq.com/docs/ai/checkly.rules.md" -OutFile ".windsurf\rules\checkly.md"
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```
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</Tab>
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</Tabs>
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You can reference the rules file using `@checkly.md` in your Windsurf chats.

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