glm-basics

glm-basics is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 38 tokens per session (599 once invoked), scanned A, original, Apache-2.0.

A guide to GLM, the General Lake Model, which simulates water temperature and mixing at different depths in a lake. It explains the model's input files, configuration, and NetCDF output format.

In plain words
What is it for?
Use it to run lake simulations, prepare meteorological and inflow data, and modify GLM parameters with Python.
Why use it?
It makes it easier to run the model and change its settings without guessing how the configuration files and boundary data fit together.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to run lake simulations, prepare meteorological and inflow data, and modify GLM parameters with Python.

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Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/glm-basics
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,764 stars · on GitHub · skillsbench.ai

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.

Any agent
npx skills add benchflow-ai/skillsbench --skill glm-basics
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for glm-basics

README.md
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/glm-basics"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/glm-basics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 599 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00038 $0.00599
Opus 5 $0.00019 $0.00300
Sonnet 5 $0.00008 $0.00120
Haiku 4.5 $0.00004 $0.00060

Measured 8d ago against content hash 77fc4c820ce8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

glm-basics 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 8d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

tasks/glm-lake-mendota/environment/skills/glm-basics/SKILL.md · 87 lines

What it actually says

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
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. 8d ago First seen · 87 lines · 38 tokens per session scan A 77fc4c820ce8

Subscribe to this mod's changes

glm-basics is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 599 once invoked, about $0.0002 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 2026-09-03.