Lab Inventory Predictor

Predict depletion time of critical lab reagents based on historical usage frequency, and automatically generate purchase alerts when stock falls below safety thresholds.

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

• Python 3.8+ is strictly required (uses dataclasses module). On Python 3.6 the script will fail at import with ModuleNotFoundError. Upgrade with pyenv install 3.8 or conda create -n lab python=3.8.
• The script should include a version guard: if sys.version_info < (3, 8): sys.exit('Error: Python 3.8+ required') before the dataclasses import.
• No external dependencies (uses only standard library)

pip install -r requirements.txt

When to Use

• Predict when lab reagents will run out based on historical consumption data
• Generate purchase alerts before reagents deplete below safety thresholds
• Track stock levels and usage history for multiple reagents
• Generate inventory reports in text, JSON, or CSV format

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

Parameter	Type	Default	Required	Description
--action	string	-	Yes	Action: status, add-reagent, record-usage, alerts, report
--data-file	string	~/.openclaw/workspace/data/lab-inventory.json	No	Path to inventory data file (must be within workspace; ../ paths rejected)

add-reagent Action

Parameter	Type	Default	Required	Description
--name	string	-	Yes	Reagent name
--current-stock	float	-	Yes	Current stock quantity
--unit	string	-	Yes	Unit of measurement (ml, mg, etc.)
--safety-days	int	7	No	Safety buffer days
--lead-time-days	int	3	No	Expected delivery time
--safety-stock	float	-	No	Safety stock threshold

record-usage Action

Parameter	Type	Default	Required	Description
--name	string	-	Yes	Reagent name
--amount	float	-	Yes	Amount consumed
--experiment	string	-	No	Experiment identifier

report Action

Parameter	Type	Default	Required	Description
--output, -o	string	stdout	No	Output file path
--format	string	text	No	Output format (text, json, csv)

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

1. Time-based: When days_until_depletion <= safety_days + lead_time_days
2. Stock-based: When current_stock <= safety_stock

Confidence Warning

When a reagent has fewer than 3 usage records, the prediction is flagged as LOW_CONFIDENCE. The output will include:

"Warning: Only [N] usage records available for [reagent]. Prediction reliability is low — collect more usage data before relying on this estimate."

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:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs (including LOW_CONFIDENCE flags for sparse data, noted inline per reagent)
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If --data-file path contains ../ or points outside the workspace, reject with a path traversal warning.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

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:

• Constraints checklist (compliance, performance, error paths)
• Unresolved items with explicit blocking reasons

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