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 agentmods add rules/psu-efd/pyhmt2d/cli-usagegit clone --depth 1 https://github.com/psu-efd/pyHMT2DWhat 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 | $0.00000 | $0.00613 |
| Opus 5 | $0.00000 | $0.00307 |
| Sonnet 5 | $0.00000 | $0.00123 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
cli-usage 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command-Line Interface Usage
This rule covers the command-line interface (CLI) tools available in pyHMT2D.
Available Commands
Location: pyHMT2D/cli/
Model Calibration
hmt-calibrate calibration.json
- Runs model calibration using the specified configuration file
- Supports both SRH-2D and HEC-RAS models
- Outputs calibration results and statistics
Model Conversion
hmt-ras-to-srh Muncie2D.p01.hdf Terrain/TerrainMuncie_composite.tif srh_Muncie
- Converts HEC-RAS 2D case to SRH-2D format
- Requires HDF file and terrain data
- Outputs SRH-2D compatible files
Result Processing
hmt-process-results results.hdf --output vtk
- Processes model results
- Converts to various formats (VTK, CSV, etc.)
- Generates analysis reports
Configuration Files
Calibration Configuration
{
"model_type": "SRH-2D",
"parameters": {
"manning_n": {
"initial": 0.03,
"min": 0.01,
"max": 0.1
}
},
"objective_function": {
"type": "rmse",
"observed_data": "observed.csv"
}
}
Parametric Study Configuration
{
"study_type": "single_parameter",
"parameter": "manning_n",
"values": [0.01, 0.02, 0.03, 0.04, 0.05],
"output_format": "vtk"
}
Common Options
Most commands support these options:
--verbose: Enable detailed output--output-dir: Specify output directory--format: Choose output format--parallel: Enable parallel processing
Examples
- Basic Calibration:
hmt-calibrate config.json --output-dir results
- Model Conversion with Custom Options:
hmt-ras-to-srh input.hdf terrain.tif output --format vtk --parallel
- Result Processing with Multiple Formats:
hmt-process-results results.hdf --format vtk,csv,excel
Error Handling
Common error messages and solutions:
- File not found
- Check file paths in configuration
- Verify file permissions
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 · 110 lines · 0 tokens per session scan A f578191128ce
cli-usage is a cursor rule published in the GitHub repository psu-efd/pyHMT2D (128 stars, last pushed 22d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 613 tokens. 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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