Lab Inventory Predictor

Function Overview

This skill is used for laboratory inventory management, predicting reagent depletion time by analyzing historical usage frequency, and automatically generating reminders when purchases are needed.

Core Capabilities

1. Inventory Tracking - Record current reagent stock levels
2. Usage Frequency Analysis - Calculate consumption rate based on experiment records
3. Depletion Prediction - Predict reagent depletion date based on consumption rate
4. Purchase Alerts - Generate alerts before reagents are about to deplete
5. Safety Stock Alerts - Alert when inventory falls below safety threshold

Usage

Command Line Call

# View all reagent status
openclaw skill lab-inventory-predictor --action status

# Add or update reagent information
openclaw skill lab-inventory-predictor --action add-reagent \
  --name "PBS Buffer" \
  --current-stock 500 \
  --unit "ml" \
  --safety-days 7

# Record experiment consumption
openclaw skill lab-inventory-predictor --action record-usage \
  --name "PBS Buffer" \
  --amount 50 \
  --experiment "Cell Culture Experiment #2024-001"

# Get purchase alerts
openclaw skill lab-inventory-predictor --action alerts

# Generate prediction report
openclaw skill lab-inventory-predictor --action report

Python API

from skills.lab_inventory_predictor import InventoryPredictor

# Initialize
predictor = InventoryPredictor("/path/to/inventory.json")

# Add reagent
predictor.add_reagent(
    name="PBS Buffer",
    current_stock=500,
    unit="ml",
    safety_days=7,
    lead_time_days=3
)

# Record usage
predictor.record_usage("PBS Buffer", 50, "Experiment #001")

# Get prediction
prediction = predictor.predict_depletion("PBS Buffer")
print(f"Predicted depletion time: {prediction['depletion_date']}")

# Get purchase alerts
alerts = predictor.get_alerts()

Parameters

Global Parameters

add-reagent Action

record-usage Action

report Action

Data Structure

Reagent Record

{
  "name": "PBS Buffer",
  "current_stock": 500,
  "unit": "ml",
  "safety_stock": 100,
  "safety_days": 7,
  "lead_time_days": 3,
  "usage_history": [
    {
      "date": "2024-01-15",
      "amount": 50,
      "experiment": "Cell Culture #001"
    }
  ],
  "daily_consumption_rate": 10.5,
  "predicted_depletion_date": "2024-02-01",
  "last_updated": "2024-01-15T10:30:00"
}

Prediction Algorithm

Consumption Rate Calculation

daily_consumption = Σ(usage_amount) / days_span

Depletion Date Prediction

days_until_depletion = current_stock / daily_consumption
depletion_date = today + days_until_depletion

Purchase Alert Trigger Conditions

1. Based on depletion time: When days_until_depletion <= safety_days + lead_time_days
2. Based on safety stock: When current_stock <= safety_stock

Configuration File

Default data storage location: ~/.openclaw/workspace/data/lab-inventory.json

Configuration example:

{
  "settings": {
    "default_safety_days": 7,
    "default_lead_time_days": 3,
    "prediction_lookback_days": 30
  },
  "reagents": []
}

Dependencies

• Python >= 3.8
• No external dependencies (uses only standard library)

Version History

• v1.0.0 (2024-02) - Initial version, supports basic prediction and alert functions

Author: OpenClaw Skill Framework
License: MIT

Risk Assessment

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

# Python dependencies
pip install -r requirements.txt

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