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 malue-ai/dazee-small --skill remindergit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/reminder)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/reminder"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/reminder/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/malue-ai/dazee-small/reminder"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/reminder.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 | $0.00017 | $0.00562 |
| Opus 5 | $0.00009 | $0.00281 |
| Sonnet 5 | $0.00003 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
reminder 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.
What it actually says
自然语言提醒
用自然语言设置提醒，自动对接平台原生提醒系统（macOS 提醒事项 / Windows 任务计划 / 系统通知）。
使用场景
- 用户说「下午 3 点提醒我开会」「明天早上提醒我买牛奶」
- 用户说「半小时后提醒我喝水」「10 分钟后提醒我看邮件」
- 用户说「每天早上 9 点提醒我吃药」
- 用户说「周五前提醒我交报告」
执行方式
时间解析
将自然语言时间转换为具体时间点：
| 用户表达 | 解析结果 |
|---|---|
| 「下午 3 点」 | 今天 15:00 |
| 「明天早上」 | 明天 09:00 |
| 「半小时后」 | 当前时间 + 30min |
| 「周五」 | 本周五 09:00 |
| 「每天早上 9 点」 | 循环提醒，每日 09:00 |
平台对接
macOS — 优先使用 Apple Reminders（需 apple-reminders skill）：
osascript -e '
tell application "Reminders"
set newReminder to make new reminder in list "提醒事项" with properties ¬
{name:"开会", due date:date "2026-02-26 15:00:00", body:"下午 3 点的会议"}
end tell'
Windows — 使用任务计划程序（需 task-scheduler skill）或系统通知。
通用回退 — 使用 scheduled-tasks skill 设置延时通知：
→ 到时间后调用系统通知 skill 发送提醒
流程
用户：下午 3 点提醒我开会
→ 解析：今天 15:00，提醒内容「开会」
→ 确认：好的，已设置提醒 ⏰ 今天 15:00 提醒你「开会」
→ 到时间后发送系统通知
输出规范
- 设置后立即确认，显示具体时间
- 模糊时间（如「下午」）默认为合理时间点并告知用户
- 循环提醒明确说明频率
- 提醒到期时通过系统通知推送，不仅在聊天中显示
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 First seen · 70 lines · 17 tokens per session scan A 578f214f334c
reminder is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 562 once invoked, about $0.0001 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 2026-09-03.
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