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 ddpie/lark-mcp-on-agentcore --skill lark-workflow-standup-reportgit clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcoreWrote 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/ddpie/lark-mcp-on-agentcore/lark-workflow-standup-report)<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-standup-report"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-standup-report/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/ddpie/lark-mcp-on-agentcore/lark-workflow-standup-report"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-standup-report.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 | $0.00055 | $0.01324 |
| Opus 5 | $0.00028 | $0.00662 |
| Sonnet 5 | $0.00011 | $0.00265 |
| Haiku 4.5 | $0.00006 | $0.00132 |
Grade A, and why
lark-workflow-standup-report 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 10d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
日程待办摘要工作流
(authentication is handled automatically by the MCP server)
适用场景
- "今天有什么安排" / "今天的日程和待办"
- "明天有什么会" / "明日日程与未完成任务"
- "帮我看看今天要做什么" / "早报摘要"
- "开工摘要" / "standup report"
- "这周还有哪些安排"
前置条件
仅支持 user 身份。
工作流
{date} ─┬─► lark_calendar_agenda [start/end] ──► 日程列表(会议/事件)
└─► lark_task_get_my_tasks(complete="false") [due_end] ──► 未完成待办列表
│
▼
AI 汇总(时间转换 + 冲突检测 + 排序)──► 摘要
Step 1: 获取日程
# 今天(默认,无需额外参数)
lark_calendar_agenda()
# 指定日期范围(必须使用 ISO 8601 格式,不支持 "tomorrow" 等自然语言)
lark_calendar_agenda(start="2026-03-26T00:00:00+08:00", end="2026-03-26T23:59:59+08:00")
注意:
start/end仅支持 ISO 8601 格式(如2026-01-01或2026-01-01T15:04:05+08:00)和 Unix timestamp,不支持"tomorrow"、"next monday"等自然语言。需要 AI 根据当前日期自行计算目标日期。
输出包含:event_id、summary、start_time(含 timestamp + timezone)、end_time、free_busy_status、self_rsvp_status。
Step 2: 获取未完成待办
# 默认 pending 摘要:必须显式过滤未完成任务(最多 20 条)
lark_task_get_my_tasks(complete="false")
# 只看指定日期前到期的未完成任务(推荐用于摘要场景,减少数据量)
lark_task_get_my_tasks(complete="false", due_end="2026-03-27T23:59:59+08:00")
# 获取全部未完成任务(超过 20 条时)
lark_task_get_my_tasks(complete="false", page_all=true)
注意:
lark_task_get_my_tasks不带complete时会同时返回已完成和未完成任务,会把已完成任务当成"待办"展示进摘要里。站会/日报这种 pending 汇总场景必须显式带上complete="false",不要省略。数据量层面也建议加过滤:
- 用
due_end过滤出目标日期前到期的任务- 如果也需要无截止日期的任务,可不加
due_end,但 AI 汇总时只展示近 30 天内创建的,其余折叠为"其他 N 项历史待办"
Step 3: AI 汇总
将 Step 1 和 Step 2 的结果整合,按以下结构输出:
## {日期}摘要({YYYY-MM-DD 星期X})
### 日程安排
| 时间 | 事件 | 组织者 | 状态 |
|------|------|--------|------|
| 09:00-10:00 | 产品需求评审 | 张三 | 已接受 |
| 14:00-15:00 | 技术方案讨论 | 李四 | 待确认 |
### 待办事项
- [ ] {task_summary}(截止:{due_date})
- [ ] {task_summary}
### 小结
- 共 {n} 场会议,{m} 项待办
- 冲突提醒:{列出时间重叠的日程}
- 空闲时段:{free_slots}(根据日程推算)
数据处理规则:
- 时间转换:API 返回 Unix timestamp,需根据
timezone字段(通常为Asia/Shanghai)转换为HH:mm格式 - RSVP 状态映射:
API 值 显示文案 accept已接受 decline已拒绝 needs_action待确认 tentative暂定 - 日程排序:按开始时间升序排列
- 冲突检测:按时间排序后,检查相邻日程是否有时间重叠(前一个 end_time > 后一个 start_time),有则在小结中列出冲突组
- 已拒绝日程:标注"已拒绝"但不计入忙碌时段和冲突检测
- 待办排序:按截止时间升序,已过期的标注"已过期",无截止时间的排在最后
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.
- 10d ago First seen · 114 lines · 55 tokens per session scan A 23fbc01a14f0
lark-workflow-standup-report is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 12d ago), licensed MIT. It adds 55 tokens to every session and 1,324 once invoked, about $0.0003 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 2026-08-31.
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