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run_g2

Automate G2 experiment execution and configuration for channel flow control analysis.

Updated Mar 17, 2026
Search Tags:#data analysis#simulation#parameter tuning#G2 experiment#channel flow control
One-click install
npx skills add https://github.com/Under-no-Flag/CrowdModel --skill run-g2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run_g2
Source: https://github.com/Under-no-Flag/CrowdModel/tree/main/.claude/skills/run_g2
Command: npx skills add https://github.com/Under-no-Flag/CrowdModel --skill run-g2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, matplotlib, pandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the execution and configuration of G2 experiments, streamlining the analysis of channel flow control and optimizing efficiency, safety, and balance.

Core Features & Use Cases

  • G2 Experiment Execution: Run full G2 scans with various direction configurations for channel flow control.
  • Custom Simulation Parameters: Modify simulation steps, time horizon, grid resolution, and other parameters.
  • Objective Weight Adjustment: Adjust weights for efficiency, safety, and balance to optimize experiment outcomes.
  • Direction Setting Configuration: Customize direction settings for individual experiments.
  • Single Case Running: Execute specific cases or the full scan for detailed analysis.
  • Result Interpretation: Analyze generated outputs for efficiency, safety, and balance metrics.

Quick Start

Run the full G2 scan with default settings:

python codes/g2_runner.py --output-root codes/results/g2_multistage_direction_scan

Frequently Asked Questions about run_g2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate channel flow control simulation for G2 experiments?▼

To run G2 experiment simulations, execute the g2_runner.py script with a specified output directory. This automates the channel flow control analysis and generates detailed results for efficiency, safety, and balance metrics.

How can I adjust simulation parameters like time horizon and grid resolution?▼

You can modify simulation parameters by configuring custom options in the g2_runner.py execution. This allows you to change simulation steps, time horizon, and grid resolution to optimize your channel flow control experiment outcomes.

What Python libraries are required for G2 experiment execution?▼

G2 experiment execution requires Python along with numpy, matplotlib, and pandas libraries. These dependencies provide the necessary simulation, data manipulation, and visualization capabilities for channel flow control analysis.

Can I run a single specific case instead of a full G2 direction scan?▼

Yes, you can execute specific single cases instead of running the full G2 scan. This allows for detailed analysis of individual direction setting configurations without processing the entire parameter space.

How do I optimize efficiency, safety, and balance metrics in channel flow control?▼

You optimize these metrics by adjusting objective weights during the G2 experiment configuration. Modifying the weights for efficiency, safety, and balance directly influences the optimization outcomes in the generated results.

What are the limitations of using Python for G2 experiment parameter tuning?▼

The primary limitation is the strict dependency on Python and specific simulation libraries like numpy, pandas, and matplotlib. Users must ensure these dependencies are correctly installed to avoid execution failures during parameter tuning.

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