glm-simulation

Guidance for running the General Lake Model (GLM), a scientific computer model that simulates water temperature and mixing through a lake’s depth. It uses configuration files and weather or water-flow input data, then produces NetCDF scientific output files.

In plain words
What is it for?
Use it to configure simulation dates, lake layers, light, mixing, weather, and initial water profiles, then run GLM with CSV forcing data and inspect its output.
Why use it?
Running the model requires the executable, correctly structured settings, and suitable input data. This explains the required files and the main configuration areas.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/cxcscmu/skilllearnbench/glm-simulation
Any agent
npx skills add cxcscmu/SkillLearnBench --skill glm-simulation
Clone the repo
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench

Made for: Claude Code, Codex.

Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 875 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.00875
Opus 5 $0.00010 $0.00438
Sonnet 5 $0.00004 $0.00175
Haiku 4.5 $0.00002 $0.00088

Measured 2d ago against content hash 56deef401a38, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

glm-simulation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/b1-one-shot-claude-haiku-4-5/temperature-simulation/glm-simulation/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GLM Simulation Skill

Overview

The General Lake Model (GLM) is a 1-D hydrodynamic lake model that simulates vertical water temperature and mixing. It reads configuration from a Fortran namelist file (.nml) and produces NetCDF output.

Installation & Setup

Prerequisites

  • GLM executable must be available in your PATH or current directory
  • Configuration file in Fortran namelist format (.nml)
  • Forcing data (meteorology, inflows, outflows) as CSV files

Configuration File Structure

The GLM configuration file contains multiple namelist sections:

&glm_setup
  sim_name = 'Lake Name'
  max_layers = 500
  min_layer_vol = 0.025
  min_layer_thick = 0.10
  max_layer_thick = 0.50
/
&light
  Kw = 0.3          ! Light extinction coefficient [0.1-0.5]
/
&mixing
  coef_mix_hyp = 0.5  ! Hypolimnetic mixing coefficient [0.3-0.7]
/
&meteorology
  wind_factor = 1.0   ! Wind speed scaling [0.7-1.3]
  lw_factor = 1.0     ! Longwave radiation scaling [0.7-1.3]
  ch = 0.0013         ! Heat transfer coefficient [0.0005-0.002]
/
&time
  start = '2009-01-01 12:00:00'
  stop = '2015-12-30 12:00:00'
  dt = 3600
/
&init_profiles
  the_depths = 0, 1, 2, ...
  the_temps = 5.1, 5.1, 5.1, ...
/

Running GLM

Basic Command

glm -f glm3.nml

The model reads the configuration and produces output (typically NetCDF) to the directory specified in &output.

Key Parameters for Calibration

These 5 parameters can be modified within specified ranges:

  1. Kw (light extinction): [0.1, 0.5] - affects light penetration
  2. coef_mix_hyp (hypolimnetic mixing): [0.3, 0.7] - affects deep water mixing
  3. wind_factor: [0.7, 1.3] - scales wind speed forcing
  4. lw_factor: [0.7, 1.3] - scales longwave radiation
  5. ch (heat exchange): [0.0005, 0.002] - affects surface heat transfer

Modifying Parameters

To modify a parameter in the .nml file:

import re

def update_nml_parameter(nml_file, section, param, value):
    """Update a parameter in a Fortran namelist file"""
    with open(nml_file, 'r') as f:
        content = f.read()

    # Find the section and update the parameter
    pattern = r'(&' + section + r'.*?)(\s+' + param + r'\s*=\s*)([^,\n]+)'
    replacement = r'\g<1>\g<2>' + str(value)
    content = re.sub(pattern, replacement, content, flags=re.DOTALL)

    with open(nml_file, 'w') as f:
        f.write(content)

Read the full file on GitHub · 102 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 102 lines · 19 tokens per session scan A 56deef401a38

Subscribe to this mod's changes

glm-simulation is a skill published in the GitHub repository cxcscmu/SkillLearnBench (82 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 875 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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