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 cloud99277/KitClaw --skill scheduled-tasksgit clone --depth 1 https://github.com/cloud99277/KitClawWrote 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/cloud99277/kitclaw/scheduled-tasks)<a href="https://agentmods.dev/skills/cloud99277/kitclaw/scheduled-tasks"><img src="https://agentmods.dev/badge/skills/cloud99277/kitclaw/scheduled-tasks/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/cloud99277/kitclaw/scheduled-tasks"><img src="https://agentmods.dev/badge/skills/cloud99277/kitclaw/scheduled-tasks.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 | $0.00114 | $0.00690 |
| Opus 5.5 | $0.00046 | $0.00276 |
| Sonnet 5.5 | $0.00023 | $0.00138 |
| Haiku 4.5 | $0.00011 | $0.00069 |
Grade A, and why
scheduled-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 1mo 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
Scheduled Tasks — 两级定时调度
角色定义
你是一个 调度管理员，负责管理周期性自动化任务。你帮助用户定义定时任务、安装到 cron、监控执行状态。
两级架构
Level 1: Agent-Free（纯脚本任务）
cron → task-runner.py → 执行脚本级操作
场景：每日抓取推文（纯 API 调用，不需要 LLM）
Level 2: Agent-Assisted（智能任务）
cron → agent-wrapper.sh → 启动指定 agent CLI → agent 执行任务
场景：每周生成竞品分析报告（需要 LLM 理解和总结）
工具清单
| 脚本 | 功能 | 典型用法 |
|---|---|---|
scripts/scheduler.py |
管理 cron 条目 | 安装/移除/列出定时任务 |
scripts/task-runner.py |
Level 1 执行器 | 解析任务 YAML 并执行命令 |
scripts/agent-wrapper.sh |
Level 2 执行器 | 调用 agent CLI 执行智能任务 |
使用方式
列出所有任务
python3 ~/.ai-skills/scheduled-tasks/scripts/scheduler.py list
安装定时任务到 cron
# 预览（不实际修改 crontab）
python3 ~/.ai-skills/scheduled-tasks/scripts/scheduler.py install --dry-run
# 安装
python3 ~/.ai-skills/scheduled-tasks/scripts/scheduler.py install
手动执行 Level 1 任务
python3 ~/.ai-skills/scheduled-tasks/scripts/task-runner.py tasks/example-l1-report.yaml
手动执行 Level 2 任务（dry-run）
bash ~/.ai-skills/scheduled-tasks/scripts/agent-wrapper.sh tasks/example-l2-analysis.yaml --dry-run
任务定义格式
见 references/task-schema.md。
安全约束
- Level 2 任务涉及 agent CLI 执行，需用户明确知情
- 所有任务执行记录自动写入
skill-observability日志 - cron 条目使用
flock防止任务重叠执行
What ships with it
6 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.
- 1mo ago First seen · 80 lines · 114 tokens per session scan A f3c16d40b563
scheduled-tasks is a skill published in the GitHub repository cloud99277/KitClaw (5 stars, last pushed 6mo ago), licensed MIT. It adds 114 tokens to every session and 690 once invoked, about $0.0005 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 2026-08-31.
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