How to Build Three Practical Claude Code Routines

Claude Code Routines can run repeatable development tasks in the cloud on a schedule, through an API call, or after a supported GitHub event. This guide builds three useful starting points: a weekly documentation check, a pull-request review, and an alert-triage routine.

Routines are currently a research-preview feature, so controls and limits may change. Check the official Routines documentation before relying on an exact menu or API shape.

Before creating a routine

Open Claude Code Routines and confirm that Claude Code on the web is enabled for your account or organization. A routine combines a prompt, one or more repositories, optional connectors, and at least one trigger.

Start with the least access the job needs. Use a test repository or read-only connector where possible, protect credentials, and require human review before code is merged, messages are sent externally, or support replies reach customers. The same review discipline applies when using Claude Code in VS Code.

Routine 1: Detect documentation drift every week

Goal: identify recently changed code whose public behavior, configuration, or API documentation may not have been updated.

• Trigger: weekly schedule, such as Monday morning.
• Repository access: read access to the code and documentation repositories.
• Optional output: a Slack channel or issue tracker. Use a draft or private engineering channel during testing.

A bounded prompt can say:

Review files merged to the main branch during the previous seven days. For each changed public API, user-visible behavior, or configuration option, locate the related documentation. Report only cases where the code changed and the relevant documentation did not. Return at most 10 findings in a table with code path, documentation path, evidence, and suggested action. If no drift is supported by the repository evidence, return “No documentation drift found.” Do not edit files or open pull requests.

Run it manually before enabling the schedule. Confirm the time window, branch, output destination, and no-result behavior. False positives often come from generated files, tests, lockfiles, or internal-only changes, so exclude those paths when appropriate.

Routine 2: Review selected pull requests

Goal: add a structured first-pass review when a pull request meets your filters. Keep a human reviewer responsible for approval and merge decisions.

• Trigger: a supported pull-request event, such as an opened or updated PR.
• Filters: target branch, repository, labels, authors, and paths as narrowly as the interface allows.
• Access: start with repository read access and permission to comment. Do not grant write access unless the workflow genuinely needs it.

Example instructions:

Review only the changes in this pull request. Check correctness, security-sensitive behavior, error handling, tests, and compatibility with the repository guidelines. Cite the affected file and line for every finding. Separate blocking issues from suggestions. Do not modify code, push commits, approve, or merge the pull request. If no actionable issue is supported by the diff and repository context, say so briefly.

Test with a small PR containing one known issue and one harmless change. Verify that the routine triggers only under the intended conditions, does not run on excluded bots or paths, and does not invent problems outside the diff. Update the PR once more to confirm that subsequent runs use the expected context.

If your organization prefers automation stored alongside the repository, compare Routines with Claude Code GitHub Actions. A CI workflow is often easier to version, audit, and reproduce.

Routine 3: Triage an alert through an API trigger

Goal: turn a monitoring alert into a concise investigation draft for the on-call engineer.

• Trigger: the routine's authenticated API endpoint.
• Inputs: a sanitized alert summary, service name, environment, time window, and a link or identifier for the source event.
• Access: read-only monitoring data and repository history. If an issue tracker is connected, create a draft or clearly labeled triage item rather than making production changes.

Analyze the supplied alert. Summarize the observed symptom, severity signals, and relevant evidence. Compare the event time with recent changes to the affected service, but do not claim causation without evidence. List up to three plausible causes and the next verification step for each. Create no deployment and change no production setting. If the payload lacks the required service, environment, or time window, return a missing-inputs report.

Protect the bearer token for the API trigger, validate the sender, and avoid placing secrets or unnecessary customer data in the payload. Test with recorded or synthetic alerts before connecting the production monitoring system.

A pre-activation checklist

1. Confirm that the trigger fires only for the intended schedule, event, or API caller.
2. Limit repository, connector, network, and branch permissions.
3. Define what the routine must not do, not just what it should do.
4. Set a bounded time range, result count, and output format.
5. Handle empty data and missing inputs explicitly.
6. Run a manual test and inspect the full input, output, duration, and usage.
7. Make a human review mandatory for consequential actions.

Monitor the first runs

Check the run history after activation. Look for unexpected trigger volume, repeated failures, broad repository scans, weak evidence, and outputs posted to the wrong destination. Pause the routine if its scope or permissions are unclear, then adjust one variable at a time and test again.

Keep a routine only if it produces a repeatable, inspectable result. For one-off local work or access to uncommitted files, a desktop task may be a better fit; for repository-owned automation, GitHub Actions may be clearer. Evaluate the tool by verified outcomes, as you would in a practical AI coding-agent test.