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/dropfan/claude-code-plugins/lark-workflow-standup-reportnpx skills add DropFan/claude-code-plugins --skill lark-workflow-standup-reportgit clone --depth 1 https://github.com/DropFan/claude-code-pluginsWrote 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/dropfan/claude-code-plugins/lark-workflow-standup-report)<a href="https://agentmods.dev/skills/dropfan/claude-code-plugins/lark-workflow-standup-report"><img src="https://agentmods.dev/badge/skills/dropfan/claude-code-plugins/lark-workflow-standup-report.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 | $0.00053 | $0.01665 |
| Opus 5 | $0.00026 | $0.00833 |
| Sonnet 5 | $0.00011 | $0.00333 |
| Haiku 4.5 | $0.00005 | $0.00167 |
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 5d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚙️ Cowork / Claude Desktop 执行环境说明(自动注入)
本技能依赖本地
lark-cli(@larksuite/cli,可用command -v lark-cli定位)及其~/.lark-cli登录态(应用密钥存于 macOS keychain)。在 Cowork 中运行任何
lark-cli命令时,必须在本地 macOS 上执行(使用 Desktop Commander 的start_process/interact_with_process,或其它本地 shell 工具),不要用隔离的 Linux 沙箱mcp__workspace__bash——沙箱里没有 lark-cli、也读不到 keychain。 执行前确保 npm 全局 bin 目录(npm prefix -g输出目录下的bin)在 PATH 中。(在 Claude Code 中可忽略本说明,lark-cli 在本机 shell 直接可用。)
日程待办摘要工作流
CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
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。执行前确保已授权:
lark-cli auth login --domain calendar,task
工作流
{date} ─┬─► calendar +agenda [--start/--end] ──► 日程列表(会议/事件)
└─► 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-cli 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-cli 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-cli 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-cli 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-cli task +get-my-tasks --complete=false --page-all
注意:
+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"
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.
- 5d ago First seen · 134 lines · 53 tokens per session scan A a7b223bf49e3
lark-workflow-standup-report is a skill published in the GitHub repository DropFan/claude-code-plugins (7 stars, last pushed 29d ago), licensed MIT. It adds 53 tokens to every session and 1,665 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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