Borrowing it
Nothing to install: this file belongs to psu-efd/pyHMT2D. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/psu-efd/pyHMT2D/main/.agents/skills/hmt-calibrate/SKILL.mdgit clone --depth 1 https://github.com/psu-efd/pyHMT2DWrote 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/psu-efd/pyhmt2d/hmt-calibrate)<a href="https://agentmods.dev/skills/psu-efd/pyhmt2d/hmt-calibrate"><img src="https://agentmods.dev/badge/skills/psu-efd/pyhmt2d/hmt-calibrate.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00623 |
| Opus 5 | $0.00022 | $0.00311 |
| Sonnet 5 | $0.00009 | $0.00125 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
hmt-calibrate 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.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run automated Manning's n calibration against observed water surface elevations.
Prerequisites
- Project opened from a
base_case/subdirectory - Observation CSV: columns
x, y, wseorname, x, y, wse(comment lines start with#) - Solver paths configured in
hmt_config.json
Steps
-
Check that a project is open. If not, ask the user to run
/hmt-openpointing tobase_case/<project_file>. -
Validate the observation file. Ask the user for the observation CSV path (e.g.,
HWMs.dat).hmt-cli check_observation_format --args '{"csv_file": "<path>"}'Show the preview table. On error: report and ask for a corrected path.
-
Show available materials.
hmt-cli get_materials -
Ask the user for calibration parameters. For each material to calibrate:
- Material name, minimum n, maximum n, optional initial guess
-
Build validated parameter specifications.
hmt-cli build_param_specs --args '{"specs": [ {"type": "manning_n", "material_name": "<name>", "min": <min>, "max": <max>, "initial": <init>}, ... ]}'Show the specs and confirm with the user.
-
Optional: single test evaluation at initial values to verify the setup.
hmt-cli evaluate_parameters --args '{"param_specs": <specs_with_initial_values>, "observation_csv": "<path>"}'If RMSE = 1e6, the simulation failed — check
pyHMT2D.logbefore proceeding. -
Ask for settings: number of iterations (default: automatic) and method (
"gp"or"nelder-mead"). -
Run the calibration.
hmt-cli run_calibration --args '{"param_specs": <specs>, "observation_csv": "<path>", "n_iterations": <N>, "method": "gp"}'Periodically show progress while waiting:
tail -20 calib_progress.log -
Report results: best parameter values table, best RMSE, iterations completed, history CSV path.
Troubleshooting:
- RMSE = 1,000,000 → check
pyHMT2D.logfor solver errors - Material not found → name must match exactly; copy from
hmt-cli get_materialsoutput
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 · 64 lines · 44 tokens per session scan A fb41dd252b5a
hmt-calibrate is a skill published in the GitHub repository psu-efd/pyHMT2D (131 stars, last pushed 29d ago), licensed MIT. It adds 44 tokens to every session and 623 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 2026-08-30.
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