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Equipment Maintenance Log

通过本地 Python 命令行脚本记录移液器、天平、离心机等设备的校准日期与校准间隔，支持新增设备、检查待维护项和列出全部设备，输出维护计划、过期告警及 30/60/90 天到期提醒；材料未显示邮件或即时消息推送通道。

aipoch-ai
aipoch-ai
热度 79.36
最近更新 2026-05-11 17:34:12
分类：自动化依赖：代码执行 / 读写文件应用场景：任务管理 / 日程安排 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add equipment-maintenance-log-2
Metadata
name
equipment-maintenance-log
description
Track lab equipment calibration dates and send maintenance reminders
version
1.0.0
category
Operations
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Equipment Maintenance Log

Track calibration dates for pipettes, balances, centrifuges and send maintenance reminders.

Usage

python scripts/main.py --add "Pipette P100" --calibration-date 2024-01-15 --interval 12
python scripts/main.py --check

Parameters

Parameter Type Default Required Description
--add string - * Equipment name to add
--calibration-date string - * Last calibration date (YYYY-MM-DD)
--interval int - * Calibration interval in months
--check flag - ** Check for upcoming maintenance
--list flag - ** List all equipment

* Required when adding equipment
** Alternative to --add (mutually exclusive)

Output

  • Maintenance schedule
  • Overdue alerts
  • Upcoming reminders (30/60/90 days)

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python/R scripts executed locally Medium
Network Access No external API calls Low
File System Access Read input files, write output files Medium
Instruction Tampering Standard prompt guidelines Low
Data Exposure Output files saved to workspace Low

Security Checklist

  • No hardcoded credentials or API keys
  • No unauthorized file system access (../)
  • Output does not expose sensitive information
  • Prompt injection protections in place
  • Input file paths validated (no ../ traversal)
  • Output directory restricted to workspace
  • Script execution in sandboxed environment
  • Error messages sanitized (no stack traces exposed)
  • Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • Successfully executes main functionality
  • Output meets quality standards
  • Handles edge cases gracefully
  • Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • Performance optimization
    • Additional feature support