lark-attendance

lark-attendance is a skill for Claude Code from DropFan/claude-code-plugins. It costs 19 tokens per session (674 once invoked), scanned A, original, MIT.

A tool for checking an employee's own Feishu attendance and clock-in records. Feishu is a workplace collaboration platform that also provides work-time tracking.

In plain words
What is it for?
Use it to look up personal clock-in and attendance history in Feishu.
Why use it?
It gives an agent a defined way to retrieve attendance information while limiting the operation to the logged-in employee's records.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code.

Part of the lark plugin — 28 skills shipped together

Good fit Use it to look up personal clock-in and attendance history in Feishu.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dropfan/claude-code-plugins/lark-attendance
Install

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.

Any agent
npx skills add DropFan/claude-code-plugins --skill lark-attendance
Clone the repo
git clone --depth 1 https://github.com/DropFan/claude-code-plugins

Made for: Claude Code.

Or install lark, the plugin that ships this one along with the rest of its 28 skills.

Wrote 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.

agentmods badge for lark-attendance

README.md
[![agentmods](https://agentmods.dev/badge/skills/dropfan/claude-code-plugins/lark-attendance/github.svg)](https://agentmods.dev/skills/dropfan/claude-code-plugins/lark-attendance)
Your own site
<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/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.

agentmods 80×15 button for lark-attendance

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 674 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.00674
Opus 5.5 $0.00008 $0.00270
Sonnet 5.5 $0.00004 $0.00135
Haiku 4.5 $0.00002 $0.00067

Measured 1mo ago against content hash 995bb296f5c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

plugins/lark/skills/lark-attendance/SKILL.md · 69 lines

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
Changes

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.

  1. 1mo ago First seen · 69 lines · 19 tokens per session scan A 995bb296f5c0

Subscribe to this mod's changes

lark-attendance is a skill published in the GitHub repository DropFan/claude-code-plugins (8 stars, last pushed 4d ago), licensed MIT. It adds 19 tokens to every session and 674 once invoked, about $0.0001 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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