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-attendancenpx skills add DropFan/claude-code-plugins --skill lark-attendancegit 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-attendance)<a href="https://agentmods.dev/skills/dropfan/claude-code-plugins/lark-attendance"><img src="https://agentmods.dev/badge/skills/dropfan/claude-code-plugins/lark-attendance.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 | $0.00019 | $0.00674 |
| Opus 5 | $0.00010 | $0.00337 |
| Sonnet 5 | $0.00004 | $0.00135 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
lark-attendance 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 3d 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
⚙️ 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。）
attendance (v1)
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
默认参数自动填充规则
调用任何 API 时，以下参数 必须自动填充，禁止向用户询问：
| 参数 | 固定值 | 说明 |
|---|---|---|
employee_type |
"employee_no" |
employee_type始终等于"employee_no" |
user_ids |
[]（空数组） |
user_ids始终等于[] |
填充示例
当构建 --params 参数时，自动注入上述字段：
employee_type保持"employee_no"不变
当构建 --data 参数时，自动注入上述字段：
{
"user_ids": [],
...用户提供的参数
}
注意：
user_ids数组保持为空[]，employee_type保持"employee_no"不变。
API Resources
lark-cli schema attendance.<resource>.<method> # 调用 API 前必须先查看参数结构
lark-cli attendance <resource> <method> [flags] # 调用 API
重要：使用原生 API 时，必须先运行
schema查看--data/--params参数结构，不要猜测字段格式。
user_tasks
query— 查询用户考勤打卡记录
权限表
| 方法 | 所需 scope |
|---|---|
user_tasks.query |
attendance:task:readonly |
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.
- 3d ago First seen · 69 lines · 19 tokens per session scan A 995bb296f5c0
lark-attendance is a skill published in the GitHub repository DropFan/claude-code-plugins (7 stars, last pushed 28d ago), licensed MIT. It adds 19 tokens to every session and 674 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 2026-08-31.
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