← Back to Skills Marketplace
wang2y

work attendance skill

by wang2y · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
91
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install analyzing-attendance-record-zhzh
Description
Analyze the weekly attendance records of employees at Zhuihui Branch and generate an attendance report in docx format. At the same time, output an xlsx file...
README (SKILL.md)

Attendance record analysis for Zhuihui Branch

Automated analysis of attendance record data.

Input Requirements

The expected input of attendance data should be in xlsx format. The first two rows are headers (Row 1: group headers, Row 2: column names). Data starts from Row 3. The columns are:

Basic Info (基本信息)

--姓名:employee's name information

--工号:employee ID number (may be '-' for some employees)

--部门:department information of the employees, including: '珠晖支行营业部', '新湘支行', '火车站支行', '茶山支行', '茅坪支行', '滨江支行', '珠晖支行本部'.

--日期:the date format is YYYYMMDD.

Clock-in Info (打卡信息)

--上班1打卡时间:primary clock-in time, format HH:MM. '-' or empty means no clock-in.

--下班1打卡时间:primary clock-out time, format HH:MM. '-' or empty means no clock-out.

--上班2/下班2/上班3/下班3打卡时间:additional clock-in/out pairs (usually empty, used for split shifts).

--考勤结果:The attendance results are classified into these categories: '正常', '缺卡', '迟到', '早退', '迟到早退', and '-' (no attendance data, typically for excluded or absent employees).

Time Statistics (时长统计)

--应出勤(小时):expected work hours per day.

--计薪时长(小时):paid hours.

--实际出勤(小时):actual work hours.

--迟到时长(分钟):late duration in minutes.

--早退时长(分钟):early leave duration in minutes.

--加班时长(小时):overtime hours.

Data Quality Check

1.Group and count by department for the statistics.

2.'新湘支行' needs to remove '珠晖-新湘-王治国' and '珠晖-新湘-魏紫兰','茶山支行' needs to remove '珠晖-茶山-陈妍','火车站支行' needs to remove '珠晖-周文娟','珠晖支行本部' needs to remove '陈喆' and '刘一婧' and '谭国球' and '谭庆荣' and '谢清林'. Additionally, ensure '陈迪' is adjusted to '茶山支行' for the output statistical analysis regardless of their original department.

3.The main office of '珠晖支行本部' consists of four departments, namely '综合管理部', '业务管理部', '公司业务部', and '个人金融部'. The staff of '综合管理部' are: '彭涵'、'谢祎'、'王友'、'蒋蕙' and '朱亚民'. '业务管理部' are: '彭国柱'、'肖冬梅'、'邓意平'、'王知生'、'颜慧松'、'李娜'、'刘新恒' and '李卿'. '公司业务部' are: '陈瑶'、'周鹍'、'甘健生'、'管巧林' and '欧海滨'. '个人金融部' are: '刘平'、'廖欢'、'陈杰'、'吴欣钰'、'费鸿平' and '汤锋'. Please divide the main office of '珠晖支行本部' into these four departments to record the attendance records.

Branch Work Schedule

Each branch has a different work schedule. The AI must apply the correct schedule when calculating expected clock-ins.

Standard branches (Monday to Friday, 5 days/week)

The following branches work Monday to Friday. Each employee is expected to clock in 10 times per week (2 times/day × 5 days).

--火车站支行 --茅坪支行 --滨江支行 --珠晖支行本部 (including all 4 sub-departments: 综合管理部, 业务管理部, 公司业务部, 个人金融部)

Weekend-rotation branches: 新湘支行 and 茶山支行 (Monday to Saturday coverage)

These branches maintain Saturday coverage through a rotation system, but the total required clock-ins for EVERY employee in these branches remains 10 times per week (2 times/day × 5 working days).

--Regular staff: Work Monday to Friday (10 clock-ins/week). --Weekend-rotation staff: Work Saturday and typically rest one day during Monday to Friday. Due to this compensatory leave, they will appear to have missed clock-ins on a weekday. The AI must correctly judge this: as long as their total actual clock-ins reach 10 times across Monday to Saturday, the weekday "absences" are normal rest days and should NOT be counted as missing cards (缺卡).

Full-coverage branch: 珠晖支行营业部 (7-day coverage)

This branch maintains coverage on all 7 days through a rotation system:

--Regular staff: Work Monday to Friday (10 clock-ins/week). --Weekend-rotation staff (3 people): Work Saturday and Sunday, and rest 1–2 days during Monday to Friday. Their expected clock-ins depend on their actual schedule:

  • If rest 2 weekdays: work 5 days total → 10 clock-ins/week
  • If rest 1 weekday: work 6 days total → 12 clock-ins/week

How to identify weekend-rotation staff: Check the Excel data for employees in 珠晖支行营业部 who have clock-in records on Saturday (周六) and/or Sunday (周日). Cross-reference with their weekday records to determine how many weekdays they rested, then calculate the correct expected clock-ins accordingly.

Holiday exceptions

All the above schedules are for normal working weeks only. During public holidays, attendance should follow the specific holiday schedule. See references/special-attendance-analysis-rules.md for holiday rules.

Mainly Analyzed Data

Calculate attendance rate:

--attendance rate = The actual number of check-ins by the department / The number of times the department should clock in × 100%

Calculating "the number of times the department should clock in": Sum up the expected clock-ins for each employee in the department, based on their individual schedule (see Branch Work Schedule above). Do NOT assume a uniform number for all employees — weekend-rotation staff may have different expected clock-ins than regular staff.

Sort by department attendance rate.

Output the attendance rate, the number of required clock-ins, the actual clock-ins, the number of missed clock-ins, the personnel who missed clock-ins, as well as the details of those who were late or left early for each department.

Special Attendance Analysis Rules

If user asks about '特殊考勤情况分析', read references/special-attendance-analysis-rules.md for performance-based actions and constraints.

Usage Guidance
Summary of what to consider before installing/running: - Functionality: The skill appears to do what it says (attendance analysis for the named branches) and includes a substantial Python script implementing the business rules. The SKILL.md and examples are consistent with the script's logic (exclusions, sub-department splitting, weekend-rotation rules). - Packaging gaps (reason for 'suspicious'): The package has no install specification or dependency list. The script requires Python and the openpyxl package (it errors out if openpyxl is missing), and SKILL.md promises .docx and .xlsx outputs but the included script only advertises structured JSON output — there is no explicit code or declared dependency to create .docx/.xlsx files. You (or the agent runtime) will likely need to install Python packages manually (openpyxl, and possibly python-docx or a writer for Excel files) or implement the conversion. This mismatch can cause runtime surprises. - Security & privacy: The skill processes sensitive HR data (employee names, IDs, attendance). The included code does not appear to make network calls or exfiltrate data (no requests/urllib/sockets in the provided code), but you should still: (1) review the full script yourself to confirm there are no hidden network callbacks, (2) run it in an isolated/sandbox environment if possible, and (3) avoid sharing the raw Excel with untrusted services. - Recommended actions before use: - Inspect the full analyze-attendance.py for any network I/O or os.system/subprocess calls (the provided snippet shows none, but verify the remainder of the file). - Ensure you have a trusted Python runtime and install required packages (pip install openpyxl and any library you choose for generating .docx/.xlsx). Consider using a virtual environment. - If you expect the skill to autonomously write .docx/.xlsx, either add or confirm code to perform those writes (and include any needed libraries) or be prepared to convert the JSON output to docx/xlsx yourself. - Because the publisher is unknown, prefer running on non-production or anonymized test data first. - What would change this assessment: If the skill metadata were updated to declare required Python packages and an install spec (or included code that actually writes .docx/.xlsx), the packaging concerns would be resolved and the verdict could move to benign. Conversely, discovery of network/credential access in the rest of the code would increase severity and confidence toward malicious.
Capability Analysis
Type: OpenClaw Skill Name: analyzing-attendance-record-zhzh Version: 1.0.0 The skill bundle is a legitimate tool for analyzing employee attendance records for the Zhuihui Branch. It consists of a Python script (analyze-attendance.py) that processes Excel files using the openpyxl library and detailed Markdown instructions (SKILL.md, special-attendance-analysis-rules.md) for the AI agent. The logic focuses on data cleaning, department-specific schedule calculations, and report generation. There is no evidence of malicious intent, data exfiltration, or unauthorized system access.
Capability Assessment
Purpose & Capability
The name, SKILL.md, and the included Python script all target the same task (analyzing attendance Excel files). However, SKILL.md requires the agent to produce a .docx report and an .xlsx supplement while the included script advertises producing structured JSON for AI report generation; no code or declared dependency is provided to generate .docx/.xlsx files. Also the skill manifest declares no install or dependency information despite the script requiring openpyxl. This is an incoherence between claimed outputs and the actual artifacts in the bundle.
Instruction Scope
The runtime instructions are narrowly scoped to reading the provided Excel, applying branch-specific rules, and producing departmental metrics. They reference only local included files (the references and examples). No instructions request external endpoints or unrelated system files. However, SKILL.md expects creation of .docx/.xlsx outputs but doesn't specify how to create them nor include helper code/libraries for that; the agent may be expected to perform that conversion itself without guidance.
Install Mechanism
There is no install spec in the registry. The Python file imports openpyxl and prints an error and exit if it's missing; openpyxl (and possibly libraries to write .docx/.xlsx such as python-docx or openpyxl write usage) are required at runtime but are not declared in the skill metadata. Lack of an install mechanism or dependency list means the operator must manually install runtime packages. This is a packaging/operational risk (surprises at runtime), though not a clear malicious indicator.
Credentials
The skill does not request environment variables, credentials, or config paths. The code reads only the provided Excel file and local reference files. No secrets or unrelated service credentials are requested.
Persistence & Privilege
The skill is not always-enabled, does not request persistent platform privileges, and does not modify other skills' configurations. It can be invoked by the agent (normal behavior) but has no elevated or permanent presence.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install analyzing-attendance-record-zhzh
  3. After installation, invoke the skill by name or use /analyzing-attendance-record-zhzh
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
1.0.0
Metadata
Slug analyzing-attendance-record-zhzh
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is work attendance skill?

Analyze the weekly attendance records of employees at Zhuihui Branch and generate an attendance report in docx format. At the same time, output an xlsx file... It is an AI Agent Skill for Claude Code / OpenClaw, with 91 downloads so far.

How do I install work attendance skill?

Run "/install analyzing-attendance-record-zhzh" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is work attendance skill free?

Yes, work attendance skill is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does work attendance skill support?

work attendance skill is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created work attendance skill?

It is built and maintained by wang2y (@wang2y); the current version is v1.0.0.

💬 Comments