What is glm-calibration?

A calibration method for GLM, a lake-temperature simulation model, that adjusts physical parameters to make simulated temperatures closer to observed temperatures. The difference is measured using RMSE, a score for prediction error.

It is for tuning light, mixing, wind, radiation, and heat-transfer parameters in water-temperature simulations.

It helps identify parameter values that reduce the gap between the model and real measurements.

glm-calibration — Skill for Claude Code and Codex by xuansenpa1

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 xuansenpa1/skillrevise --skill glm-calibration

Clone the repo

git clone --depth 1 https://github.com/xuansenpa1/skillrevise

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/glm-calibration/github.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/glm-calibration)

Your own site

<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/glm-calibration"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/glm-calibration/github.svg" alt="Measured on agentmods" height="20"></a>

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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/glm-calibration"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/glm-calibration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 839 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod 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.

Model	Per session	Once invoked
Fable 5.1	$0.00032	$0.00839
Opus 5	$0.00016	$0.00419
Sonnet 5	$0.00006	$0.00168
Haiku 4.5	$0.00003	$0.00084

Measured 8d ago against content hash a1342ccb6297, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

glm-calibration scanned grade A with 1 finding 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(['glm'], capture_output=True)

Origin

This is a copy

100% identical to glm-calibration — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skillsbench/tasks/glm-lake-mendota/environment/skills/glm-calibration/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GLM Calibration Guide

Overview

GLM calibration involves adjusting physical parameters to minimize the difference between simulated and observed water temperatures. The goal is typically to achieve RMSE < 2.0°C.

Key Calibration Parameters

Parameter	Section	Description	Default	Range
`Kw`	`&light`	Light extinction coefficient (m⁻¹)	0.3	0.1 - 0.5
`coef_mix_hyp`	`&mixing`	Hypolimnetic mixing coefficient	0.5	0.3 - 0.7
`wind_factor`	`&meteorology`	Wind speed scaling factor	1.0	0.7 - 1.3
`lw_factor`	`&meteorology`	Longwave radiation scaling	1.0	0.7 - 1.3
`ch`	`&meteorology`	Sensible heat transfer coefficient	0.0013	0.0005 - 0.002

Parameter Effects

Parameter	Increase Effect	Decrease Effect
`Kw`	Less light penetration, cooler deep water	More light penetration, warmer deep water
`coef_mix_hyp`	More deep mixing, weaker stratification	Less mixing, stronger stratification
`wind_factor`	More surface mixing	Less surface mixing
`lw_factor`	More heat input	Less heat input
`ch`	More sensible heat exchange	Less heat exchange

Calibration with Optimization

from scipy.optimize import minimize

def objective(x):
    Kw, coef_mix_hyp, wind_factor, lw_factor, ch = x

    # Modify parameters
    params = {
        'Kw': round(Kw, 4),
        'coef_mix_hyp': round(coef_mix_hyp, 4),
        'wind_factor': round(wind_factor, 4),
        'lw_factor': round(lw_factor, 4),
        'ch': round(ch, 6)
    }
    modify_nml('glm3.nml', params)

    # Run GLM
    subprocess.run(['glm'], capture_output=True)

    # Calculate RMSE
    rmse = calculate_rmse(sim_df, obs_df)
    return rmse

# Initial values (defaults)
x0 = [0.3, 0.5, 1.0, 1.0, 0.0013]

# Run optimization
result = minimize(
    objective,
    x0,
    method='Nelder-Mead',
    options={'maxiter': 150}
)

Read the full file on GitHub · 93 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.

8d ago First seen · 93 lines · 32 tokens per session scan A a1342ccb6297

Subscribe to this mod's changes

glm-calibration is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 32 tokens to every session and 839 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to glm-calibration, differing in 0 lines, and is treated as a copy.

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glm-calibration