equipment-maintenance-log
Track lab equipment calibration dates and send maintenance reminders
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/equipment-maintenance-log-2Equipment 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aipoch-ai-equipment-maintenance-log-2": {
"enabled": true,
"auto_update": true
}
}
}Related Skills
mechanism-flowchart
Generates Mermaid flowchart code and visual diagrams for pathophysiological.
reference-style-sync
One-click synchronization and standardization of reference formats in literature management tools, intelligently fixing metadata errors.
clinical-data-cleaner
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.
metagenomic-krona-chart
Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple question types, difficulty levels, and anatomical regions. Supports gross anatomy, neuroanatomy, and clinical correlations for self-assessment and exam preparation.