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/ebfe_validate_models/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/ebfe_validate_models)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/ebfe_validate_models"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/ebfe_validate_models/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/ebfe_validate_models"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/ebfe_validate_models.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 | $0.00173 | $0.02276 |
| Opus 5.5 | $0.00069 | $0.00910 |
| Sonnet 5.5 | $0.00035 | $0.00455 |
| Haiku 4.5 | $0.00017 | $0.00228 |
Grade A, and why
ebfe_validate_models 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
eBFE Model Validator Skill
Purpose
When the user asks to validate an organized eBFE or BLE model, use this skill to verify the model is actually runnable via ras-commander's built-in dataframe validation capabilities.
Why This Matters: Organizing files alone is insufficient -- verify the HEC-RAS project is actually runnable using ras-commander's own validation.
Validation Using ras-commander Dataframes
Step 1: Initialize Project
from ras_commander import init_ras_project
from pathlib import Path
# Initialize organized model
ras = init_ras_project(organized_ras_folder, version)
# If this succeeds, basic project structure is valid
# Now check dataframes for path validity
Step 2: Validate plan_df
Check all plan file references:
print("Validating Plans (plan_df)...")
for idx, row in ras.plan_df.iterrows():
plan_number = row['plan_number']
checks = {}
# Check plan file exists
plan_file = Path(row['plan_file'])
checks['plan_file_exists'] = plan_file.exists()
# Check geometry file exists
if 'geom_file' in row and pd.notna(row['geom_file']):
geom_file = Path(row['geom_file'])
checks['geom_file_exists'] = geom_file.exists()
# Check flow file exists
if 'flow_file' in row and pd.notna(row['flow_file']):
flow_file = Path(row['flow_file'])
checks['flow_file_exists'] = flow_file.exists()
# Check HDF file exists (pre-run results)
if 'hdf_path' in row and pd.notna(row['hdf_path']):
hdf_file = Path(row['hdf_path'])
checks['hdf_exists'] = hdf_file.exists()
if hdf_file.exists():
print(f" ✓ Plan {plan_number}: Pre-run results found")
# Check for absolute paths (CRITICAL)
checks['no_absolute_paths'] = not plan_file.is_absolute()
if all(v for k, v in checks.items() if v is not None):
print(f" ✓ Plan {plan_number}: All checks passed")
else:
failed = [k for k, v in checks.items() if v is False]
print(f" ✗ Plan {plan_number}: Failed checks: {failed}")
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 Changed · +5 lines 9a56d5a03203
- 1mo ago First seen · 276 lines · 173 tokens per session scan A 7127b1eecaf3
ebfe_validate_models is a skill published in the GitHub repository gpt-cmdr/ras-commander (88 stars, last pushed today), licensed MIT. It adds 173 tokens to every session and 2,276 once invoked, about $0.0007 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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