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 cxcscmu/SkillLearnBench --skill netcdf-analysisgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/netcdf-analysis)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/netcdf-analysis"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/netcdf-analysis.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00024 | $0.00312 |
| Opus 5 | $0.00012 | $0.00156 |
| Sonnet 5 | $0.00005 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
netcdf-analysis 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 3d 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.
What it actually says
NetCDF Analysis for GLM Output
Reading GLM Output
import netCDF4 as nc
import numpy as np
import pandas as pd
ds = nc.Dataset('output/output.nc')
temp = ds.variables['temp'][:] # masked array [time, layers]
z = ds.variables['z'][:] # height above bottom [time, layers]
time_var = ds.variables['time']
times = nc.num2date(time_var[:], time_var.units)
Extracting Temperature at Specific Depths
GLM uses variable layer heights. For each timestep:
lake_depth = 25 # from morphometry (crest_elev - min(H))
for t in range(len(times)):
valid = ~temp[t].mask if hasattr(temp[t], 'mask') else np.ones(temp.shape[1], bool)
depths_from_surface = lake_depth - z[t, valid]
temps = temp[t, valid]
# Interpolate to desired depth
RMSE Calculation
# Merge on exact datetime and rounded depth
# rmse = sqrt(mean((obs - sim) ** 2))
Key Notes
- GLM z is height from lake bottom; depth = lake_depth - z
- Round depths to nearest integer for matching
- Use exact datetime matching (no nearest-time)
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.
- 3d ago First seen · 42 lines · 24 tokens per session scan A 596b94c00cd4
netcdf-analysis is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 312 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 2026-09-03.
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