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_extract_results/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_extract_results)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/hecras_extract_results"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_extract_results/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_extract_results"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_extract_results.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 77 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00201 | $0.02795 |
| Opus 5 | $0.00101 | $0.01398 |
| Sonnet 5 | $0.00040 | $0.00559 |
| Haiku 4.5 | $0.00020 | $0.00280 |
Grade A, and why
hecras_extract_results 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 11d 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 — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting HEC-RAS Results
When the user asks to extract HEC-RAS results, use the patterns below. Read the primary sources for complete details -- do not duplicate their content here.
Primary Sources:
- HDF Class Reference:
ras_commander/hdf/AGENTS.md- Canonical HDF package contract, module families, lazy loading rules, decorators - Library Context:
ras_commander/AGENTS.md- HDF architecture overview, subpackage organization - Example Notebooks:
examples/400_1d_hdf_data_extraction.ipynb- 1D cross section results (unsteady)examples/410_2d_hdf_data_extraction.ipynb- 2D mesh results (comprehensive)examples/401_steady_flow_analysis.ipynb- Steady state results (complete workflow)examples/420_breach_results_extraction.ipynb- Dam breach results
- Code Docstrings: All HDF classes have comprehensive docstrings with parameter details
Quick Start
Minimal Working Example
from ras_commander import init_ras_project, HdfResultsPlan, HdfResultsMesh
# Initialize project
init_ras_project("path/to/project", "7.0")
# Check simulation type
is_steady = HdfResultsPlan.is_steady_plan("01")
# Extract results based on type
if is_steady:
profiles = HdfResultsPlan.get_steady_profile_names("01")
wse = HdfResultsPlan.get_steady_wse("01", profile_name="100 year")
else:
max_wse = HdfResultsMesh.get_mesh_maximum("01", variable="Water Surface")
Navigation Guide
1. Architecture & Organization
Read First: ras_commander/hdf/AGENTS.md
Read this first for:
- 18 HDF classes and their organization
- Module structure (Core, Geometry, Results, Infrastructure, Visualization)
- Class hierarchy and dependencies
- Lazy loading patterns for heavy dependencies
- Decorator usage (
@staticmethod,@log_call,@standardize_input) - File type expectations (plan_hdf vs geom_hdf)
- Common HDF paths in files
Key Sections:
- Module families and common entry points
- Implementation rules for decorators, lazy loading, and HDF input handling
- Input and output rules for flexible plan/path/HDF handles
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.
- 11d ago First seen · 330 lines · 201 tokens per session scan A e55be611db1f
hecras_extract_results is a skill published in the GitHub repository gpt-cmdr/ras-commander (79 stars, last pushed today), licensed MIT. It adds 201 tokens to every session and 2,795 once invoked, about $0.0010 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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