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 seaworld008/Commonly-used-high-value-skills --skill lark-attendancegit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skillsWrote 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/seaworld008/commonly-used-high-value-skills/lark-attendance)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-attendance"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/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.
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-attendance"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-attendance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 67 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 96 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00027 | $0.01059 |
| Opus 5 | $0.00014 | $0.00530 |
| Sonnet 5 | $0.00005 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
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 9d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
Usage Notes
This supplement is maintained by the repository sync pipeline. It keeps the imported upstream skill usable inside this curated collection when the upstream source is intentionally concise.
Common Patterns
1. Confirm that the user's task matches the skill trigger.
2. Read the relevant project files or user-provided context before acting.
3. Choose the smallest reversible action that advances the task.
4. Run the verification command or manual check that proves the result.
5. Report the outcome, evidence, and any remaining risk.
Boundaries
- Prefer the upstream workflow for Lark Attendance; this section only adds local quality guardrails.
- Do not invent project facts when required files, vaults, services, or tools are unavailable.
- Stop and ask for clarification when the next action could overwrite user work, expose private data, or change production state.
- Treat skill selection as routing, not ceremony: invoke only the narrowest applicable workflow and keep user or repository instructions authoritative.
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.
- 9d ago First seen · 118 lines · 27 tokens per session scan A 73ec85309143
lark-attendance is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 5d ago), licensed MIT. It adds 27 tokens to every session and 1,059 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 2026-09-03.
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