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glm-basics

Basic usage of the General Lake Model (GLM) for lake temperature simulation

Install / Use

npx skills add benchflow-ai/skillsbench --skill glm-basics

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

79/100

Category

Marketing

Supported Platforms

Universal

Tags

Our assessment of glm-basics

glm-basics scores 79/100 on our quality scale, 362nd of 440 Marketing skills we index.

Its SKILL.md is 2.1 KB long, well organised into 9 sections with 3 code examples: moderately detailed.

With 1,813 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
20/30
Structure
18/20
Description
12/15
Adoption
14/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so glm-basics is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

glm-basics compared with similar skills

All 4 of these similar skills score higher than glm-basics; compare them before choosing.

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Frequently asked questions

How do I install glm-basics?
Run npx skills add benchflow-ai/skillsbench --skill glm-basics. The install tabs above show the steps for each supported agent.
Which AI agents does glm-basics work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is glm-basics safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is glm-basics still maintained?
The repository was last updated about 2 months ago, so glm-basics is actively maintained.

name: glm-basics description: Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters. license: MIT

GLM Basics Guide

Overview

GLM (General Lake Model) is a 1D hydrodynamic model that simulates vertical temperature and mixing dynamics in lakes. It reads configuration from a namelist file and produces NetCDF output.

Running GLM

cd /root
glm

GLM reads glm3.nml in the current directory and produces output in output/output.nc.

Input File Structure

| File | Description | |------|-------------| | glm3.nml | Main configuration file (Fortran namelist format) | | bcs/*.csv | Boundary condition files (meteorology, inflows, outflows) |

Configuration File Format

glm3.nml uses Fortran namelist format with multiple sections:

&glm_setup
   sim_name = 'LakeName'
   max_layers = 500
/
&light
   Kw = 0.3
/
&mixing
   coef_mix_hyp = 0.5
/
&meteorology
   meteo_fl = 'bcs/meteo.csv'
   wind_factor = 1
   lw_factor = 1
   ch = 0.0013
/
&inflow
   inflow_fl = 'bcs/inflow1.csv','bcs/inflow2.csv'
/
&outflow
   outflow_fl = 'bcs/outflow.csv'
/

Modifying Parameters with Python

import re

def modify_nml(nml_path, params):
    with open(nml_path, 'r') as f:
        content = f.read()
    for param, value in params.items():
        pattern = rf"({param}\s*=\s*)[\d\.\-e]+"
        replacement = rf"\g<1>{value}"
        content = re.sub(pattern, replacement, content)
    with open(nml_path, 'w') as f:
        f.write(content)

# Example usage
modify_nml('glm3.nml', {'Kw': 0.25, 'wind_factor': 0.9})

Common Issues

| Issue | Cause | Solution | |-------|-------|----------| | GLM fails to start | Missing input files | Check bcs/ directory | | No output generated | Invalid nml syntax | Check namelist format | | Simulation crashes | Unrealistic parameters | Use values within valid ranges |

Best Practices

  • Always backup glm3.nml before modifying
  • Run GLM after each parameter change to verify it works
  • Check output/ directory for results after each run

Related Skills

View on GitHub
GitHub Stars1.8k
CategoryMarketing
Updated2mo ago
Forks368

Languages

PDDL

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions