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Scheduled tasks

Schedule recurring tasks in ChatGPT

Schedule recurring tasks to run in the background. Review active, paused, and completed tasks and recent runs in Scheduled. You can combine scheduled tasks with skills for more complex work.

In the ChatGPT desktop app, scheduled tasks can work with local projects and run in the project directory or an isolated worktree. Keep the computer on and the app running when a scheduled task needs local files.

For example, schedule a task to evaluate telemetry errors and submit fixes, or to create reports about recent codebase changes. For ongoing work that should keep using the same context, schedule work from an existing task.

For project-scoped scheduled tasks, keep the machine powered on and the ChatGPT desktop app running. The selected project must still be available on disk when the task is scheduled to run.

In Git repositories, you can choose whether a scheduled task runs in your local project or on a new worktree. Both options run in the background. Worktrees keep changes from scheduled tasks separate from unfinished local work, while running in your local project can modify files you are still working on. In non-version-controlled projects, scheduled tasks run directly in the project directory.

You can also leave the model and reasoning effort on their default settings, or choose them explicitly if you want more control over how the scheduled task runs.

Scheduled tasks run unattended with your default sandbox settings. Start with the narrowest access that lets the task succeed, and grant network or broader file access only when required. Understand sandboxing.

Manage scheduled tasks

Find all scheduled tasks and their runs on Scheduled in the ChatGPT desktop app sidebar.

The Scheduled view acts as your inbox. Scheduled task runs with findings appear there, and an unread indicator shows when a run needs your attention.

Standalone scheduled tasks start a new task for each scheduled run and report results in Scheduled. Use them when each run should be independent or when one scheduled task should run across one or more projects. If you need a custom cadence, use the custom schedule controls. For an advanced schedule, edit its RFC 5545 recurrence rule (RRULE), such as RRULE:FREQ=MONTHLY;BYMONTHDAY=1;BYHOUR=9;BYMINUTE=0.

For Git repositories, each scheduled task can run either in your local project or on a dedicated background worktree. Use worktrees when you want to isolate scheduled-task changes from unfinished local work. Use local mode when you want the scheduled task to work directly in your main checkout, keeping in mind that it can change files you are actively editing. In non-version-controlled projects, scheduled tasks run directly in the project directory. You can have the same scheduled task run on more than one project.

Scheduled tasks in ChatGPT Work on the web or in Work or Codex in the ChatGPT desktop app can use plugins. Scheduled tasks can also use skills. To keep scheduled tasks maintainable and shareable across teams, use skills to define the action and provide tools and context. Select or invoke a specific skill in the task prompt when the workflow shouldn’t rely on automatic tool selection.

Ask ChatGPT to create or update scheduled tasks

You can create and update scheduled tasks from a Work or Codex task in ChatGPT. Describe the work, the schedule, and whether each scheduled run should return to the current task or start a new task. ChatGPT can draft the prompt, choose the right destination, and update the scheduled task when its scope or cadence changes.

For example, ask ChatGPT to schedule a follow-up from the current task while a deployment finishes, or ask it to create a standalone scheduled task that checks a project on a recurring schedule.

Skills can also create or update scheduled tasks. For example, a skill for babysitting a pull request could set up a scheduled task that checks the PR status with the GitHub plugin and fixes new review feedback.

Schedule work from a task

Schedule work from an existing task when you want ChatGPT to return to that task on a schedule. The scheduled work uses the task’s existing context instead of starting from a new prompt each time.

Scheduled work in a task can use minute-based intervals for active follow-up loops, or daily and weekly schedules when you need a check-in at a specific time.

Schedule work from a task for:

  • checking a long-running operation until it finishes
  • polling Slack, GitHub, or another connected source when the results should stay in the same task
  • reminding ChatGPT to continue a review loop at a fixed cadence
  • running a skill-driven workflow that uses plugins, such as checking PR status and addressing new feedback
  • continuing an ongoing research or triage task without losing its context

Use a standalone scheduled task when each run should be independent or when findings should appear as separate runs in Scheduled.

When you schedule work from a task, make the prompt durable. It should describe what ChatGPT should do on each scheduled run, how to decide whether there is anything important to report, and when to stop or ask you for input.

Test scheduled tasks

Before you schedule a task, test the prompt manually in a regular task first. This helps you confirm:

  • The prompt is clear and scoped correctly.
  • The selected or default model, reasoning effort, and tools behave as expected.
  • The resulting output is reviewable.

When you start scheduling runs, review the first few outputs and adjust the prompt or cadence as needed.

In the ChatGPT desktop app, you can explicitly trigger a skill in a scheduled task prompt by using $skill-name.

Worktree cleanup for scheduled tasks

If you choose worktrees for Git repositories, frequent schedules can create many worktrees over time. Archive scheduled runs you no longer need, and avoid pinning runs unless you intend to keep their worktrees.

Permissions and security model

Scheduled tasks run unattended and use your default sandbox settings.

For a plain-language explanation of these boundaries, see the sandboxing overview. For filesystem and network rules, see Permissions.

  • If your sandbox mode is read-only, tool calls fail if they require modifying files, accessing network, or working with apps on your computer. Consider updating sandbox settings to workspace write.
  • If your sandbox mode is workspace-write, tool calls fail if they require modifying files outside the workspace, accessing network, or working with apps on your computer. You can selectively allowlist commands to run outside the sandbox using rules.
  • If your sandbox mode is full access, background scheduled tasks carry elevated risk, as ChatGPT may change files, run commands, and access network without asking. Consider updating sandbox settings to workspace write, and using rules to selectively define which commands the agent can run with full access.

If you are in a managed environment, admins can restrict these behaviors using admin-enforced requirements. For example, they can disallow approval_policy = "never" or constrain allowed sandbox modes. See Admin-enforced requirements (requirements.toml).

Scheduled tasks use approval_policy = "never" when your organization policy allows it. If admin requirements disallow approval_policy = "never", scheduled tasks fall back to the approval behavior of your selected permission mode.

Examples

Automatically create new skills

Scan all of the `~/.codex/sessions` files from the past day and if there have been any issues using particular skills, update the skills to be more helpful. Personal skills only, no repo skills.

If there’s anything we’ve been doing often and struggle with that we should save as a skill to speed up future work, let’s do it.

Definitely don't feel like you need to update any- only if there's a good reason!

Let me know if you make any.

Stay up-to-date with your project

Look at the latest remote origin/master or origin/main . Then produce an exec briefing for the last 24 hours of commits that touch <DIRECTORY>

Formatting + structure:

- Use rich Markdown (H1 workstream sections, italics for the subtitle, horizontal rules as needed).
- Preamble can read something like “Here’s the last 24h brief for <directory>:”
- Subtitle should read: “Narrative walkthrough with owners; grouped by workstream.”
- Group by workstream rather than listing each commit. Workstream titles should be H1.
- Write a short narrative per workstream that explains the changes in plain language.
- Use bullet points and bolding when it makes things more readable
- Feel free to make bullets per person, but bold their name

Content requirements:

- Include PR links inline (e.g., [#123](...)) without a “PRs:” label.
- Do NOT include commit hashes or a “Key commits” section.
- It’s fine if multiple PRs appear under one workstream, but avoid per‑commit bullet lists.

Scope rules:

- Only include changes within the current cwd (or main checkout equivalent)
- Only include the last 24h of commits.
- Use `gh` to fetch PR titles and descriptions if it helps.
  Also feel free to pull PR reviews and comments

Combining scheduled tasks with skills to fix your own bugs

Create a new skill that tries to fix a bug introduced by your own commits by creating a new $recent-code-bugfix and store it in your personal skills.

---
name: recent-code-bugfix
description: Find and fix a bug introduced by the current author within the last week in the current working directory. Use when a user wants a proactive bugfix from their recent changes, when the prompt is empty, or when asked to triage/fix issues caused by their recent commits. Root cause must map directly to the author’s own changes.
---

# Recent Code Bugfix

## Overview

Find a bug introduced by the current author in the last week, implement a fix, and verify it when possible. Operate in the current working directory, assume the code is local, and ensure the root cause is tied directly to the author’s own edits.

## Workflow

### 1) Establish the recent-change scope

Use Git to identify the author and changed files from the last week.

- Determine the author from `git config user.name`/`user.email`. If unavailable, use the current user’s name from the environment or ask once.
- Use `git log --since=1.week --author=<author>` to list recent commits and files. Focus on files touched by those commits.
- If the user’s prompt is empty, proceed directly with this default scope.

### 2) Find a concrete failure tied to recent changes

Prioritize defects that are directly attributable to the author’s edits.

- Look for recent failures (tests, lint, runtime errors) if logs or CI outputs are available locally.
- If no failures are provided, run the smallest relevant verification (single test, file-level lint, or targeted repro) that touches the edited files.
- Confirm the root cause is directly connected to the author’s changes, not unrelated legacy issues. If only unrelated failures are found, stop and report that no qualifying bug was detected.

### 3) Implement the fix

Make a minimal fix that aligns with project conventions.

- Update only the files needed to resolve the issue.
- Avoid adding extra defensive checks or unrelated refactors.
- Keep changes consistent with local style and tests.

### 4) Verify

Attempt verification when possible.

- Prefer the smallest validation step (targeted test, focused lint, or direct repro command).
- If verification cannot be run, state what would be run and why it wasn’t executed.

### 5) Report

Summarize the root cause, the fix, and the verification performed. Make it explicit how the root cause ties to the author’s recent changes.

Afterward, create a new scheduled task:

Check my commits from the last 24h and submit a $recent-code-bugfix.