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 Cloudgeni-ai/opengeni --skill opengeni-schedulesgit clone --depth 1 https://github.com/Cloudgeni-ai/opengeniWrote 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/cloudgeni-ai/opengeni/opengeni-schedules)<a href="https://agentmods.dev/skills/cloudgeni-ai/opengeni/opengeni-schedules"><img src="https://agentmods.dev/badge/skills/cloudgeni-ai/opengeni/opengeni-schedules/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/cloudgeni-ai/opengeni/opengeni-schedules"><img src="https://agentmods.dev/badge/skills/cloudgeni-ai/opengeni/opengeni-schedules.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 | $0.00062 | $0.01077 |
| Opus 5.5 | $0.00025 | $0.00431 |
| Sonnet 5.5 | $0.00012 | $0.00215 |
| Haiku 4.5 | $0.00006 | $0.00108 |
Grade A, and why
opengeni-schedules 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a schedule
Turn the user's description into scheduled work in the current workspace. Research what is available before asking questions. Ask only for essential details you cannot discover or reasonably decide from the request.
Choose the destination
By default, schedule a message in this conversation. “Remind me”, “check later”,
“keep watching” and “schedule yourself” mean continue this chat. Supply name,
schedule and prompt; the tool resolves the current chat from its signed
context. Choose another existing chat with targetSessionId only when requested.
Its model, tools, machine and attachments apply at run time, so do not copy those
settings into the schedule.
Use reusable_session or new_session_per_run with agentConfig only when the
user requests separate-agent work. Sessionless calls require an explicit
destination or a separate-agent mode.
Discover what the task needs
- Use
scheduled_tasks_listto avoid duplicating an existing schedule. Follow pagination when needed and usescheduled_tasks_getfor a relevant task's details. A list summary is not its complete configuration. - If the work needs a repository, use
github_repositories_listto find the authorized repository and select the required resources. - If the scheduled agent needs credentials, use
secret_searchto find the saved item and write its reference (for example{{secret:GitHub/token}}) in the prompt. Save it to the workspace withsecret_saveorsecret_movewhen the schedule runs without you; never copy secret values into prompts. - If it needs an integration such as Slack or Sentry, use
capability_catalog_searchand the available tool discovery to find the capability and exact operations. Follow returned connection/setup and approval requirements. Do not claim that discovery means the integration is ready.
Select only the resources and tools the task needs; saved secrets are used by
{{secret:…}} reference and need no attachment. Missing
optional dependencies are not a reason to ask for unnecessary setup. If a
required capability or authority is unavailable, explain the remaining setup
briefly instead of inventing a tool or borrowing another person's connection.
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 · +4 lines 1d909fcd72ed
- 7d ago Changed 2196bae0c3b5
- 12d ago Changed · +33 lines 612b07c75c4b
- 13d ago First seen · 57 lines · 62 tokens per session scan A 260a4f348865
opengeni-schedules is a skill published in the GitHub repository Cloudgeni-ai/opengeni (202 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 1,077 once invoked, about $0.0002 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 2026-10-03.
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