pyHMT2D: Skill for Claude Code

.agents/skills/hmt-monte-carlo/SKILL.md

hmt-monte-carlo is a skill for Claude Code, Codex from psu-efd/pyHMT2D. It costs 46 tokens per session (713 once invoked), scanned A, original, MIT.

A Monte Carlo analysis tool for hydraulic models. Monte Carlo analysis repeatedly runs a model with varied inputs to estimate the likelihood of different outcomes.

In plain words
What is it for?
Use it to sample uncertain hydraulic parameters and calculate exceedance probabilities.
Why use it?
It shows how uncertain input values can change the results instead of presenting one possibly misleading prediction.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is psu-efd/pyHMT2D's own configuration. It tells Claude Code and Codex how to work on pyHMT2D itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything pyHMT2D configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/psu-efd/pyHMT2D/main/.agents/skills/hmt-monte-carlo/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/psu-efd/pyHMT2D

Made for: Claude Code, Codex.

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README.md
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Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 713 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash ee3dffdbec22, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

.agents/skills/hmt-monte-carlo/SKILL.md · 70 lines

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

  1. Check that a project is open. If not, ask the user to run /hmt-open pointing to base_case/<project_file>.

  2. Show available materials.

    hmt-cli get_materials
    
  3. 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
  4. 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.

  5. Ask for MC settings: number of samples (50–200), random seed (default 42), output directory (default ./mc_runs), parallel processes (default 1).

  6. 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.

  7. 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
    
  8. Report: successful/failed runs, results JSON path.

  9. 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

Read the full file on GitHub · 70 lines

Changes

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.

  1. 10d ago First seen · 70 lines · 46 tokens per session scan A ee3dffdbec22

Subscribe to this mod's changes

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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