letta-ai/letta-code is an agent harness for building assistants that retain memory, identity, and experience across interactions instead of treating each task as isolated. Developers use it through local, desktop, browser, or messaging interfaces for interactive or continuously running agents, and its catalogue entries configure the agents' skills, instructions, rules, and behavior.
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 agentmods add skills/letta-ai/letta-code/scheduling-tasksnpx skills add letta-ai/letta-code --skill scheduling-tasksgit clone --depth 1 https://github.com/letta-ai/letta-codeWrote 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/letta-ai/letta-code/scheduling-tasks)<a href="https://agentmods.dev/skills/letta-ai/letta-code/scheduling-tasks"><img src="https://agentmods.dev/badge/skills/letta-ai/letta-code/scheduling-tasks.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.02604 |
| Opus 5 | $0.00024 | $0.01302 |
| Sonnet 5 | $0.00010 | $0.00521 |
| Haiku 4.5 | $0.00005 | $0.00260 |
Grade A, and why
scheduling-tasks 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 2d 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scheduling Tasks
This skill lets you create, list, and manage scheduled tasks using the letta cron CLI. Scheduled tasks send a prompt to the agent on a timer — useful for reminders, periodic check-ins, and deferred follow-ups.
When to Use This Skill
- User asks to be reminded of something ("remind me to X at Y")
- User wants a recurring check-in ("every morning ask me about X")
- User wants a one-shot delayed message ("in 30 minutes, check on X")
- User wants to see or cancel existing scheduled tasks
Where Schedules Run — Omit the Flags
Default guidance: omit --runner and --computer. The CLI places the schedule so the work keeps running on the computer where it was created. Don't move scheduled work to a different computer than the active conversation without a reason: two computers working the same conversation can conflict.
Pass a flag only when you have a requirement the default can't infer:
--runner cloud— the schedule must fire no matter which computers are online; execute in the agent's cloud sandbox.--computer <deviceId>— the work needs a specific connected computer (its filesystem, services, or credentials). Get the deviceId fromletta computers list. If that computer is offline at fire time, execution falls back to the cloud sandbox.--runner local— the work must only ever run on the current computer, even if that means missing fires while no session is running here.
The CLI reports its placement in the command output. If it warns that the schedule is local (this happens when the cloud scheduler cannot reach the current computer), the schedule only fires while a Letta session is running here — read the warning and decide whether that's acceptable.
Fast Follow-ups vs Recurring Jobs
Two patterns cover most schedules:
- Fast follow-ups ("check on the PR in 5m"): the default is right — same computer as the active conversation. If the session dies before it fires, the follow-up usually died with the task anyway.
- Recurring jobs ("every Monday 11am, start the lunch order"): prefer durability. If the CLI warned that a recurring schedule is local, that's usually wrong for the user's intent — recreate it with
--runner cloud, or--computerif the job needs a specific always-on computer. A fresh conversation per run is the default; pass a conversation explicitly when the job needs continuity in one thread.
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.
- 2d ago Changed 18bccc0be0ef
- 6d ago First seen · 233 lines · 49 tokens per session scan A 910b6b5e1994
scheduling-tasks is a skill published in the GitHub repository letta-ai/letta-code (3,215 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 2,604 once invoked, about $0.0002 per session on Opus 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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