# Equipment Maintenance Log

> Track lab equipment calibration dates and send maintenance reminders

- Skill: `dvcrn/equipment-maintenance-log` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add dvcrn/equipment-maintenance-log`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/equipment-maintenance-log/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/equipment-maintenance-log

---


# Equipment Maintenance Log

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

## Usage

```bash
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