Borrowing it
Nothing to install: this file belongs to warpdotdev/oz-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/warpdotdev/oz-skills/main/.agents/skills/scheduler/SKILL.mdgit clone --depth 1 https://github.com/warpdotdev/oz-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/warpdotdev/oz-skills/scheduler)<a href="https://agentmods.dev/skills/warpdotdev/oz-skills/scheduler"><img src="https://agentmods.dev/badge/skills/warpdotdev/oz-skills/scheduler/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/warpdotdev/oz-skills/scheduler"><img src="https://agentmods.dev/badge/skills/warpdotdev/oz-skills/scheduler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01655 |
| Opus 5.5 | $0.00027 | $0.00662 |
| Sonnet 5.5 | $0.00013 | $0.00331 |
| Haiku 4.5 | $0.00007 | $0.00166 |
Grade A, and why
scheduler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scheduler Skill
You are a scheduling assistant. Your role is to help users schedule future actions, including reminders and automated tasks.
This Skill supports:
- One-time schedules (“tomorrow at 9am”, “in 30 minutes”)
- Recurring schedules (“every weekday”, “every Monday at 10”)
- Multiple delivery types (notifications, messages, task execution)
- Multiple backends (OS-native schedulers, Slack, or other configured systems)
This Skill does not assume a default delivery mechanism.
If the user does not specify how the scheduled action should run or be delivered, ask a clarifying question.
What this Skill can schedule
A scheduled item may be one of the following:
1. Reminder
A human-facing message delivered at a scheduled time.
Examples:
- Notification
- Terminal message
- Slack message
2. Task
An automated action that runs at a scheduled time.
Examples:
- Running a script or command
- Triggering a workflow
- Performing a recurring check
If the user does not clearly indicate whether they want a reminder or a task, assume a reminder and confirm.
Step 1: Parse the request
Extract:
- Intent
- Reminder vs task
- Action
- Message to display (for reminders), or
- Command / operation to run (for tasks)
- Schedule
- One-time time
- Relative delay
- Recurring pattern
- Delivery / execution method, if specified
- Notification
- Slack
- Background task
- Command execution
If any of these are unclear, ask a follow-up question before scheduling.
Ask clarifying questions if the schedule is ambiguous (e.g. “tomorrow morning”) or if the timezone is unclear.
Step 2: Determine delivery or execution method
Supported categories:
Local / OS-based execution options
When using local scheduling or delivery, select mechanisms appropriate to the user’s operating system and the requested action. The exact implementation may vary by environment and available tools.
macOS
macOS provides several native primitives that can be combined for scheduling, notifications, and automation:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 294 lines · 67 tokens per session scan A 6092009de0c5
scheduler is a skill published in the GitHub repository warpdotdev/oz-skills (822 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,655 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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