Borrowing it
Nothing to install: this file belongs to gpt-cmdr/ras-commander. 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/gpt-cmdr/ras-commander/main/.claude/skills/hecras_parse_geometry/SKILL.mdgit clone --depth 1 https://github.com/gpt-cmdr/ras-commanderWrote 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/gpt-cmdr/ras-commander/hecras_parse_geometry)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/hecras_parse_geometry"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_parse_geometry/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/gpt-cmdr/ras-commander/hecras_parse_geometry"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_parse_geometry.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 | $0.00141 | $0.03056 |
| Opus 5.5 | $0.00056 | $0.01222 |
| Sonnet 5.5 | $0.00028 | $0.00611 |
| Haiku 4.5 | $0.00014 | $0.00306 |
Grade A, and why
hecras_parse_geometry 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 1mo 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 — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parsing HEC-RAS Geometry Files
Primary Sources (read these for details):
- Implementation guide:
ras_commander/geom/AGENTS.md(parsing algorithms, API reference) - Working examples:
examples/201_1d_plaintext_geometry.ipynbandexamples/202_2d_plaintext_geometry.ipynb(comprehensive demonstrations)
Follow these quick-reference patterns for common tasks. Read the primary sources above for implementation details, parsing algorithms, and complete API documentation.
Quick Start Patterns
List Cross Sections
from ras_commander.geom.GeomCrossSection import GeomCrossSection
# List all cross sections in file
xs_df = GeomCrossSection.get_cross_sections("model.g01")
# Returns: DataFrame with River, Reach, RS, NodeName
# Filter by river/reach
xs_df = GeomCrossSection.get_cross_sections(
"model.g01",
river="Ohio River",
reach="Reach 1"
)
Read Cross Section Geometry
# Get station-elevation profile
df = GeomCrossSection.get_station_elevation(
"model.g01",
"Ohio River",
"Reach 1",
"1000"
)
# Returns: DataFrame with Station, Elevation columns
# Get bank stations
banks = GeomCrossSection.get_bank_stations(
"model.g01",
"Ohio River",
"Reach 1",
"1000"
)
# Returns: dict with BankLeft, BankRight keys
Modify Cross Section
# Read current geometry
df = GeomCrossSection.get_station_elevation(
"model.g01",
"Ohio River",
"Reach 1",
"1000"
)
# Modify elevations (e.g., lower channel 2 feet)
df.loc[df['Station'].between(100, 200), 'Elevation'] -= 2.0
# Get bank stations
banks = GeomCrossSection.get_bank_stations(
"model.g01",
"Ohio River",
"Reach 1",
"1000"
)
# Write back (creates .bak backup, handles bank interpolation)
GeomCrossSection.set_station_elevation(
"model.g01",
"Ohio River",
"Reach 1",
"1000",
df,
bank_left=banks['BankLeft'],
bank_right=banks['BankRight']
)
Update 2D Manning's n
from ras_commander.geom.GeomLandCover import GeomLandCover
# Read land cover table
lc_df = GeomLandCover.get_base_mannings_n("model.g01")
# Returns: DataFrame with LandCoverID, ManningsN
# Modify roughness
lc_df.loc[lc_df['LandCoverID'] == 42, 'ManningsN'] = 0.15
# Write back (creates .bak backup)
GeomLandCover.set_base_mannings_n("model.g01", lc_df)
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.
- 1mo ago First seen · 401 lines · 141 tokens per session scan A e2d0423bf87e
hecras_parse_geometry is a skill published in the GitHub repository gpt-cmdr/ras-commander (88 stars, last pushed today), licensed MIT. It adds 141 tokens to every session and 3,056 once invoked, about $0.0006 per session on Opus 5.5. 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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