Lab Inventory Predictor

Lab Inventory Predictor

Predicts reagent depletion time by analyzing historical usage frequency, and automatically generates reminders when purchases are needed.

Input Validation

This skill accepts: lab reagent inventory data (stock levels, usage records) for the purpose of predicting depletion dates and generating purchase alerts.

If the user's request does not involve lab reagent inventory management or depletion prediction — for example, asking to analyze experimental results, manage equipment, or perform general data analysis — do not proceed with the workflow. Instead respond: > "lab-inventory-predictor is designed to predict reagent depletion and generate purchase alerts based on usage history. Your request appears to be outside this scope. Please provide reagent inventory data, or use a more appropriate tool for your task."

Do not continue the workflow when the request is out of scope, missing the required --action parameter, or would require unsupported assumptions. For missing inputs, state exactly which fields are missing.

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --action status

Prerequisites

pip install -r requirements.txt

When to Use

Workflow

1. Validate input — confirm the request is within scope before any processing. 2. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. 4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. 5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

# View all reagent status
python scripts/main.py --action status
Add or update reagent information
python scripts/main.py --action add-reagent \
  --name "PBS Buffer" \
  --current-stock 500 \
  --unit "ml" \
  --safety-days 7
Record experiment consumption
python scripts/main.py --action record-usage \
  --name "PBS Buffer" \
  --amount 50 \
  --experiment "Cell Culture Experiment #2024-001"
Get purchase alerts
python scripts/main.py --action alerts
Generate prediction report
python scripts/main.py --action report

Python API

from skills.lab_inventory_predictor import InventoryPredictor
predictor = InventoryPredictor("/path/to/inventory.json")
predictor.add_reagent(name="PBS Buffer", current_stock=500, unit="ml", safety_days=7, lead_time_days=3)
predictor.record_usage("PBS Buffer", 50, "Experiment #001")
prediction = predictor.predict_depletion("PBS Buffer")
print(f"Predicted depletion time: {prediction['depletion_date']}")
alerts = predictor.get_alerts()

Parameters

Global Parameters

add-reagent Action

record-usage Action

report Action

Prediction Algorithm

Consumption Rate

daily_consumption = Σ(usage_amount) / days_span

Depletion Date

days_until_depletion = current_stock / daily_consumption
depletion_date = today + days_until_depletion

Purchase Alert Trigger Conditions

Confidence Warning

Each LOW_CONFIDENCE prediction must include an inline risk note adjacent to the prediction result, not only in the aggregate Risks section.

Fallback Behavior

If scripts/main.py fails or required inputs are incomplete: 1. Report the exact failure point and error message. 2. State what can still be completed (e.g., status check without prediction). 3. Manual fallback: verify the inventory JSON file exists at the configured path, then re-run with --action status to confirm data integrity. 4. Do not fabricate execution outcomes or inventory data.

Output Requirements

Every final response must make these items explicit when relevant:

Error Handling

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits (include LOW_CONFIDENCE flag inline per reagent if fewer than 3 usage records) 7. Next Checks

For stress/multi-constraint requests, also include:

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

Command Line

# View all reagent status
python scripts/main.py --action status
Add or update reagent information
python scripts/main.py --action add-reagent \
  --name "PBS Buffer" \
  --current-stock 500 \
  --unit "ml" \
  --safety-days 7
Record experiment consumption
python scripts/main.py --action record-usage \
  --name "PBS Buffer" \
  --amount 50 \
  --experiment "Cell Culture Experiment #2024-001"
Get purchase alerts
python scripts/main.py --action alerts
Generate prediction report
python scripts/main.py --action report

Python API

from skills.lab_inventory_predictor import InventoryPredictor
predictor = InventoryPredictor("/path/to/inventory.json")
predictor.add_reagent(name="PBS Buffer", current_stock=500, unit="ml", safety_days=7, lead_time_days=3)
predictor.record_usage("PBS Buffer", 50, "Experiment #001")
prediction = predictor.predict_depletion("PBS Buffer")
print(f"Predicted depletion time: {prediction['depletion_date']}")
alerts = predictor.get_alerts()

pip install -r requirements.txt