meteorology-driver-classification

Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meani...

Security Scan

The skill is an instruction-only guide for grouping meteorological variables into driver categories; its requirements and instructions are consistent with that purpose and it does not request credentials, installs, or system access.

Purpose & Capability

Name, description, and SKILL.md all describe classifying environmental variables into categories. There are no unexpected required binaries, env vars, or config paths.

Instruction Scope

SKILL.md contains guidance, category lists, a short example Python snippet for deriving net radiation, and best practices. It does not instruct reading unrelated files, accessing environment variables, contacting external endpoints, or exfiltrating data.

Install Mechanism

No install spec and no code files — instruction-only — so nothing is written to disk or fetched at install time.

Credentials

The skill requires no environment variables, credentials, or config paths. The requested privileges are proportionate to a documentation/guide skill.

Persistence & Privilege

always is false, the skill is user-invocable and does not request permanent presence or modification of other skills or system-wide settings.

Assessment

This is a simple, coherent documentation-style skill; it is low-risk to install. Before using it in automated pipelines, ensure any datasets you feed to derived-code are appropriate (no sensitive personal data) and verify that derived-variable code matches your data schema. If you expect the agent to execute code generated from this guide, review that code before running it locally or on production data.

Driver Classification Guide

Overview

When analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning.

Common Driver Categories

Heat

Variables related to thermal energy and radiation:

• Air temperature
• Shortwave radiation
• Longwave radiation
• Net radiation (shortwave + longwave)
• Surface temperature
• Humidity
• Cloud cover

Flow

Variables related to water movement:

• Precipitation
• Inflow
• Outflow
• Streamflow
• Evaporation
• Runoff
• Groundwater flux

Wind

Variables related to atmospheric circulation:

• Wind speed
• Wind direction
• Gust speed
• Atmospheric pressure

Human

Variables related to anthropogenic activities:

• Developed area
• Agriculture area
• Impervious surface
• Population density
• Industrial output
• Land use change rate

Derived Variables

Sometimes raw variables need to be combined before analysis:

# Combine radiation components into net radiation
df['NetRadiation'] = df['Longwave'] + df['Shortwave']

Grouping Strategy

1. Identify all available variables in your dataset
2. Assign each variable to a category based on physical meaning
3. Create derived variables if needed
4. Variables in the same category should be correlated

Validation

After statistical grouping, verify that:

• Variables load on expected components
• Groupings make physical sense
• Categories are mutually exclusive

Best Practices

• Use domain knowledge to define categories
• Combine related sub-variables before analysis
• Keep number of categories manageable (3-5 typically)
• Document your classification decisions