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 OpenLAIR/OpenSkill --skill evo-glm-simulationgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-glm-simulation)<a href="https://agentmods.dev/skills/openlair/openskill/evo-glm-simulation"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-glm-simulation/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/openlair/openskill/evo-glm-simulation"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-glm-simulation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00083 | $0.00839 |
| Opus 5 | $0.00042 | $0.00419 |
| Sonnet 5 | $0.00017 | $0.00168 |
| Haiku 4.5 | $0.00008 | $0.00084 |
Grade A, and why
evo-glm-simulation 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evo-glm-simulation
End-to-end workflow for running and calibrating GLM for Lake Mendota.
Directory contract
/root/glm3.nml— namelist (working dir is/root/)/root/bcs/*.csv— meteo, inflow (yahara, pheasant), outflow/root/field_temp_oxy.csv— field obs (columns: datetime, depth, temp, OXY_oxy)/root/output/output.nc— required output
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-glm-simulation/scripts')
from utils import (read_glm_nml, update_glm_nml, verify_bcs, run_glm,
process_glm_netcdf, load_field_observations,
calculate_rmse, calibrate_parameters)
# 1) Verify boundary files
assert verify_bcs() == []
# 2) Run GLM once
ok, so, se = run_glm('/root/')
# 3) Read NetCDF & compute RMSE
sim = process_glm_netcdf('/root/output/output.nc')
obs = load_field_observations('/root/field_temp_oxy.csv')
rmse, n = calculate_rmse(sim, obs)
# 4) Calibrate to RMSE < 2.0
best, history = calibrate_parameters(target_rmse=1.95, max_iters=30)
Key concepts
Depth conversion: GLM z is height-from-bottom. Convert with
depth = z_layer_top_max - z_layer. Use dynamic z_surface per timestep
(max layer height at that timestep) rather than static lake_depth.
NS variable: number of active layers at each timestep — slice arrays
to [:NS[t]] to drop padded/masked entries.
Time units: GLM NetCDF time uses hours since <start> — parse from
the variable's units attribute (fallback: 2009-01-01 12:00:00).
RMSE matching: GLM saves daily (nsave=24); group obs by date, build a 1D interpolator over sim depths, look up obs depth, compute RMSE on matched (obs, sim) pairs.
Calibration levers (Mendota)
| Param | Block | Range | Notes |
|---|---|---|---|
Kw |
&light |
0.3–0.6 | Higher = darker water, cooler deep |
sw_factor |
&meteorology |
0.9–1.05 | Shortwave scaling |
wind_factor |
&meteorology |
0.9–1.1 | Surface mixing |
coef_mix_hyp |
&mixing |
0.3–0.7 | Deep mixing |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 74 lines · 83 tokens per session scan A c24e943d0c96
evo-glm-simulation is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed yesterday), licensed Apache-2.0. It adds 83 tokens to every session and 839 once invoked, about $0.0004 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 2026-09-11.
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