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.
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.
npx skills add benchflow-ai/skillsbench --skill glm-basicsgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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.
[](https://agentmods.dev/skills/benchflow-ai/skillsbench/glm-basics)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/glm-basics"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/glm-basics/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.
<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>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.00599 |
| Opus 5 | $0.00019 | $0.00300 |
| Sonnet 5 | $0.00008 | $0.00120 |
| Haiku 4.5 | $0.00004 | $0.00060 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- glm-basics — 100% identical, 0 lines differ
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.nmlbefore modifying - Run GLM after each parameter change to verify it works
- Check
output/directory for results after each run
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 · 87 lines · 38 tokens per session scan A 77fc4c820ce8
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.
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