// frequently asked questions
Common questions about Cron Builder
How do I write a cron expression that runs every Monday at 9 AM?
0 9 * * 1 — field order is minute, hour, day-of-month, month, day-of-week. Day-of-week 1 = Monday. This tool shows a plain-English preview ("Every Monday at 09:00") and lists the next 10 scheduled fire times so you can confirm the interval before deploying.
How is this different from crontab.guru?
crontab.guru shows you whether your expression is syntactically valid and a plain-English description of the schedule. This tool additionally shows the next 10 actual fire times as ISO timestamps — useful when you need to verify the exact execution window relative to a deadline or maintenance window. crontab.guru has no equivalent for previewing the next N executions.
What cron format does this support?
Standard 5-field POSIX cron (minute, hour, day-of-month, month, day-of-week). The @reboot, @daily, @hourly shorthand aliases are also supported. Quartz scheduler's 6-field format (with seconds) is not supported — this tool targets Unix crontabs, Kubernetes CronJobs, GitHub Actions schedules, and similar POSIX-compliant runtimes.
What timezone does "next 10 run times" use?
Your local browser timezone by default, with an option to switch to UTC. Cron expressions are timezone-agnostic by design — the tool applies your selected timezone to the computed timestamps.
Can I use this to write cron expressions for GitHub Actions?
Yes. GitHub Actions uses standard 5-field cron in UTC. Set the tool timezone to UTC before checking your next-run preview to see what the scheduler will actually execute. Note that GitHub Actions schedules can drift by up to 15 minutes under load.
Is there a reference for common cron patterns?
Below the builder is a reference table of frequently used patterns: every minute (* * * * *), every hour on the hour (0 * * * *), every day at midnight (0 0 * * *), weekdays at 9 AM (0 9 * * 1-5), first day of month (0 0 1 * *), every 15 minutes (*/15 * * * *).
What cron schedules work well for AI agent automation?
Three patterns cover most AI automation use cases. A pre-workday warmup (0 6 * * 1-5) runs a context-gathering agent before business hours start — pulling new data, summarising overnight changes, staging inputs — so the first interactive session begins with a prepared state rather than a blank slate. A staggered intra-day cycle (0 9,13,17 * * 1-5) fires agents at natural working-hours windows — useful when the agent's output depends on external services that are most responsive during business hours. A nightly checkpoint (0 22 * * *) records end-of-day state so future runs can diff against a known baseline rather than reconstructing it. For lightweight polling or health checks, */10 * * * * (every 10 minutes) balances detection speed against API call volume.