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-monte-carlo/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-monte-carlo)<a href="https://agentmods.dev/skills/psu-efd/pyhmt2d/hmt-monte-carlo"><img src="https://agentmods.dev/badge/skills/psu-efd/pyhmt2d/hmt-monte-carlo/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/psu-efd/pyhmt2d/hmt-monte-carlo"><img src="https://agentmods.dev/badge/skills/psu-efd/pyhmt2d/hmt-monte-carlo.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 | $0.00046 | $0.00713 |
| Opus 5 | $0.00023 | $0.00357 |
| Sonnet 5 | $0.00009 | $0.00143 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
hmt-monte-carlo 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 10d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Monte Carlo uncertainty analysis on a hydraulic model.
Prerequisites
- Project in a
base_case/subdirectory - Parameter uncertainty distributions defined by user
- 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>. -
Show available materials.
hmt-cli get_materials -
Ask the user for uncertain parameters. For each:
- Material name or BC ID
- Distribution:
"truncated_normal"(default) or"uniform" - For
truncated_normal: mean, std, min, max - For
uniform: min, max
-
Build validated parameter specifications.
hmt-cli build_param_specs --args '{"specs": [ {"type": "manning_n", "material_name": "<name>", "distribution": "truncated_normal", "mean": <mean>, "std": <std>, "min": <min>, "max": <max>}, ... ]}'Confirm the distributions with the user.
-
Ask for MC settings: number of samples (50–200), random seed (default 42), output directory (default
./mc_runs), parallel processes (default 1). -
Generate samples.
hmt-cli generate_mc_samples --args '{"param_specs": <specs>, "n_samples": <N>, "random_seed": <seed>, "output_csv": "mc_samples.csv"}'Show a preview of the first 5 sample rows.
-
Run Monte Carlo simulations.
hmt-cli run_monte_carlo --args '{"base_case_dir": "./base_case", "param_specs": <specs>, "n_samples": <N>, "n_processes": <procs>, "random_seed": <seed>, "sample_csv": "mc_samples.csv", "delete_cases": true, "output_dir": "<output_dir>"}'Periodically show progress while waiting:
tail -20 mc_progress.log -
Report: successful/failed runs, results JSON path.
-
Compute statistics. Ask for observation point coordinates if the user wants point statistics.
hmt-cli get_mc_statistics --args '{"results_json": "<path>", "observation_points": [{"name": "<name>", "x": <x>, "y": <y>}], "exceedance_probabilities": [99, 90, 50, 10, 1]}'Present exceedance table: Point | P99 | P90 | P50 | P10 | P1
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.
- 10d ago First seen · 70 lines · 46 tokens per session scan A ee3dffdbec22
hmt-monte-carlo is a skill published in the GitHub repository psu-efd/pyHMT2D (131 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 713 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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