Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fmind/dot --skill scheduled-jobsgit clone --depth 1 https://github.com/fmind/dotWrote 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/fmind/dot/scheduled-jobs)<a href="https://agentmods.dev/skills/fmind/dot/scheduled-jobs"><img src="https://agentmods.dev/badge/skills/fmind/dot/scheduled-jobs/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/fmind/dot/scheduled-jobs"><img src="https://agentmods.dev/badge/skills/fmind/dot/scheduled-jobs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00019 | $0.00617 |
| Opus 5.5 | $0.00008 | $0.00247 |
| Sonnet 5.5 | $0.00004 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
scheduled-jobs 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 9d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scheduled Jobs
Run an existing bounded command through the native user scheduler and prove its outcome. python-script owns program code, and mise owns task commands.
Workflow
- Prove one invocation: run the authorized command with explicit arguments and the scheduler's minimal environment. Use an existing dry-run mode when exercising real effects is outside scope; dry-run success does not prove a real run.
- Choose the native owner: use Linux user timers or macOS LaunchAgents. Keep an existing project scheduler unless migration is requested. Harness-native agent schedules remain with the matching host skill.
- Make execution bounded: use an absolute executable path, explicit working directory, locked dependencies, bounded I/O and runtime, and one writer lock for all entry points. Decide whether a missed occurrence is skipped, coalesced, or replayed; replay requires an idempotent operation.
- Prepare the configuration: call the existing command directly, keep branching in tested Python, and validate the unit or plist. Store durable job definitions in their owning repository; do not insert secrets into configuration, command lines, or logs.
- Activate within scope: reuse explicit authority for recurring effects and cost. If it is missing, present the validated definition and precise schedule for approval before enabling it; preparing a schedule does not authorize its future effects.
- Verify execution: distinguish loaded configuration, next due time, process completion, and the actual application result. Inspect a real scheduler-triggered run; a manual invocation or enabled timer is only partial evidence.
- Stop and recover: disable future triggers and separately account for any running process and uncertain external write. Resume only from current state; do not blindly replay a timed-out mutation.
Gotchas
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago Changed · -3 lines e38ecd414942
- 11d ago Changed bfbcbe77ead6
- 12d ago Changed · -1 tokens per session 1458edd06b60
- 14d ago Changed e97c0476d1b0
- 23d ago Changed a0fda2cb8048
- 26d ago Changed 2245962429b2
- 29d ago Changed · +1 lines · -14 tokens per session 197d26dfe1ff
- 1mo ago First seen · 37 lines · 34 tokens per session scan A 661591a47bd5
scheduled-jobs is a skill published in the GitHub repository fmind/dot (11 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 617 once invoked, about $0.0001 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 2026-09-10.
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