Agent skill

Glm Output

by benchflow-ai in benchflow-ai/skillsbench

Read and process GLM output files. An agent skill from benchflow-ai/skillsbench.

MITAuto-check passed

Install Glm Output

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .claude/skills/glm-output && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .claude/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .agents/skills/glm-output && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .agents/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .cursor/skills/glm-output && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .cursor/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/glm-lake-mendota/environment/skills/glm-output

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .gemini/skills/glm-output && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .gemini/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .github/skills/glm-output && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .github/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill glm-output -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench glm-output --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/glm-lake-mendota/environment/skills/glm-output .opencode/skills/glm-output && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "glm-output" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output into .opencode/skills/glm-output/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "glm-output", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: glm-output
GitHub stars: 1.8k
Token cost: ~1k tokens
SKILL.md length: 165 words
Files: 1
Skills in repo: 189
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Read and process GLM output files. An agent skill from benchflow-ai/skillsbench.

You need to extract temperature data from NetCDF output
SKILL.md covers Overview, Output File, Reading Output with Python and Coordinate Conversion, plus 5 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Convert depth coordinates

What it does

Glm Output is an agent skill from benchflow-ai/skillsbench. Read and process GLM output files. Use when you need to extract temperature data from NetCDF output, convert depth coordinates, or calculate RMSE against observations.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is MIT.

When your agent uses it

You need to extract temperature data from NetCDF output
Convert depth coordinates
Calculate RMSE against observations

Example prompts

“/glm-output”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Glm Output loads about 1k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~45
When it runs · the whole SKILL.md, loaded when a task matches: ~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its MIT licence (© benchflow-ai). 165 words, ~1,002 tokens.

Download SKILL.mdSave it as .claude/skills/glm-output/SKILL.md (or your agent's skills folder).

name: glm-output
description: Read and process GLM output files. Use when you need to extract temperature data from NetCDF output, convert depth coordinates, or calculate RMSE against observations.
license: MIT

GLM Output Guide

Overview

GLM produces NetCDF output containing simulated water temperature profiles. Processing this output requires understanding the coordinate system and matching with observations.

Output File

After running GLM, results are in output/output.nc:

Variable	Description	Shape
`time`	Hours since simulation start	(n_times,)
`z`	Height from lake bottom (not depth!)	(n_times, n_layers, 1, 1)
`temp`	Water temperature (°C)	(n_times, n_layers, 1, 1)

Reading Output with Python

python

from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

nc = Dataset('output/output.nc', 'r')
time = nc.variables['time'][:]
z = nc.variables['z'][:]
temp = nc.variables['temp'][:]
nc.close()

Coordinate Conversion

Important: GLM z is height from lake bottom, not depth from surface.

python

# Convert to depth from surface
# Set LAKE_DEPTH based on lake_depth in &init_profiles section of glm3.nml
LAKE_DEPTH = <lake_depth_from_nml>
depth_from_surface = LAKE_DEPTH - z

Complete Output Processing

python

from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

def read_glm_output(nc_path, lake_depth):
    nc = Dataset(nc_path, 'r')
    time = nc.variables['time'][:]
    z = nc.variables['z'][:]
    temp = nc.variables['temp'][:]
    start_date = datetime(2009, 1, 1, 12, 0, 0)

    records = []
    for t_idx in range(len(time)):
        hours = float(time[t_idx])
        date = pd.Timestamp(start_date) + pd.Timedelta(hours=hours)
        heights = z[t_idx, :, 0, 0]
        temps = temp[t_idx, :, 0, 0]

        for d_idx in range(len(heights)):
            h_val = heights[d_idx]
            t_val = temps[d_idx]
            if not np.ma.is_masked(h_val) and not np.ma.is_masked(t_val):
                depth = lake_depth - float(h_val)
                if 0 <= depth <= lake_depth:
                    records.append({
                        'datetime': date,
                        'depth': round(depth),
                        'temp_sim': float(t_val)
                    })
    nc.close()

    df = pd.DataFrame(records)
    df = df.groupby(['datetime', 'depth']).agg({'temp_sim': 'mean'}).reset_index()
    return df

Reading Observations

python

def read_observations(obs_path):
    df = pd.read_csv(obs_path)
    df['datetime'] = pd.to_datetime(df['datetime'])
    df['depth'] = df['depth'].round().astype(int)
    df = df.rename(columns={'temp': 'temp_obs'})
    return df[['datetime', 'depth', 'temp_obs']]

Calculating RMSE

python

def calculate_rmse(sim_df, obs_df):
    merged = pd.merge(obs_df, sim_df, on=['datetime', 'depth'], how='inner')
    if len(merged) == 0:
        return 999.0
    rmse = np.sqrt(np.mean((merged['temp_sim'] - merged['temp_obs'])**2))
    return rmse

# Usage: get lake_depth from glm3.nml &init_profiles section
sim_df = read_glm_output('output/output.nc', lake_depth=25)
obs_df = read_observations('field_temp_oxy.csv')
rmse = calculate_rmse(sim_df, obs_df)
print(f"RMSE: {rmse:.2f}C")

Common Issues

Issue	Cause	Solution
RMSE very high	Wrong depth conversion	Use `lake_depth - z`, not `z` directly
No matched observations	Datetime mismatch	Check datetime format consistency
Empty merged dataframe	Depth rounding issues	Round depths to integers

Best Practices

Check lake_depth in &init_profiles section of glm3.nml
Always convert z to depth from surface before comparing with observations
Round depths to integers for matching
Group by datetime and depth to handle duplicate records
Check number of matched observations after merge

Files

Just SKILL.md in tasks/glm-lake-mendota/environment/skills/glm-output of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Glm Output next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Glm Output compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Glm Output this skillbenchflow-ai/skillsbench	1.8k	—	~1k	Automated safety check: Pass	MIT	2 mo ago
Glm Master Skillzai-org/GLM-skills	476	—	~1.9k	Automated safety check: Notes	Apache-2.0	5 mo ago
Glm Delegationathola/claude-night-market	341	—	~1.1k	Automated safety check: Pass	MIT	3 days ago
Glm Image Genzai-org/GLM-skills	476	—	~2.9k	Automated safety check: Pass	Apache-2.0	5 mo ago
Fmri Glm Analysis GuideNeuroAIHub/BrainPilot	1.1k	—	~5.8k	Automated safety check: Pass	AGPL-3.0	7 days ago
Glm Visionarchibate/dotfiles-opencode	108	—	~795	Automated safety check: Pass	None	5 mo ago

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Questions about Glm Output

What does Glm Output do?

Read and process GLM output files. An agent skill from benchflow-ai/skillsbench. Glm Output is an agent skill from benchflow-ai/skillsbench. Read and process GLM output files.

When should I use Glm Output?

Glm Output fits situations like: you need to extract temperature data from NetCDF output; convert depth coordinates; calculate RMSE against observations.

How do I install Glm Output in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill glm-output -a claude-code`. Or copy the skill folder (tasks/glm-lake-mendota/environment/skills/glm-output in benchflow-ai/skillsbench) into .claude/skills/glm-output in your project. Claude Code loads it when a task matches its description.

How do I install Glm Output in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill glm-output -a codex`. Or copy the skill folder (tasks/glm-lake-mendota/environment/skills/glm-output in benchflow-ai/skillsbench) into .agents/skills/glm-output in your project. Codex loads it when a task matches its description.

Can I use Glm Output in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill glm-output -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/glm-output, .gemini/skills/glm-output, .github/skills/glm-output and .opencode/skills/glm-output in your project.

What does Glm Output need to run?

SKILL.md names no scripts, command-line tools or credentials: Glm Output is instructions for the agent only. Our summary lists: Python 3.

Does Glm Output access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Glm Output safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Glm Output use?

Glm Output is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Glm Output use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Glm Output?

Skills that share tags, products or a category with Glm Output: Glm Master Skill (zai-org/GLM-skills, 476 stars), Glm Delegation (athola/claude-night-market, 341 stars), Glm Image Gen (zai-org/GLM-skills, 476 stars) and Fmri Glm Analysis Guide (NeuroAIHub/BrainPilot, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Glm Output?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,834 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Glm Output

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

GLM Output Guide

Overview

Output File

Reading Output with Python

Coordinate Conversion

Complete Output Processing

Reading Observations

Calculating RMSE

Common Issues

Best Practices

Files

Compare with similar skills

Similar skills

More from benchflow-ai/skillsbench

Questions about Glm Output