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_organize_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_organize_models)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/ebfe_organize_models"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/ebfe_organize_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_organize_models"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/ebfe_organize_models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00110 | $0.04595 |
| Opus 5 | $0.00055 | $0.02298 |
| Sonnet 5 | $0.00022 | $0.00919 |
| Haiku 4.5 | $0.00011 | $0.00460 |
Grade A, and why
ebfe_organize_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 9d 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 — 560 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Organizing eBFE Models
Purpose
When the user asks to organize eBFE or BLE downloads, use this skill to transform downloaded FEMA eBFE/BLE study area files into a standardized 4-folder structure, regardless of the original archive organization patterns.
Standardized Output Structure
Every organized eBFE study area should have this structure:
{StudyAreaName}_{HUC8}/
├── HMS Model/ # HEC-HMS hydrologic models
│ ├── {ProjectName}.hms
│ ├── {ProjectName}.basin
│ ├── *.dss
│ └── ... (all HMS-related files)
├── RAS Model/ # HEC-RAS hydraulic models
│ ├── {Model1}/
│ │ ├── {Model1}.prj
│ │ ├── {Model1}.g##
│ │ ├── {Model1}.p##
│ │ └── ... (all RAS project files)
│ └── {Model2}/...
├── Spatial Data/ # GIS, terrain, geodatabases
│ ├── Terrain/
│ ├── *.tif, *.hdf
│ ├── *.gdb/
│ └── ... (all spatial data)
└── Documentation/ # Reports, metadata, inventories
├── *.pdf
├── *.xlsx (inventories)
├── *_metadata.xml
└── ... (all documentation)
Input Patterns
Handle these downloaded archive patterns:
Pattern 1: Multiple 1D models in variable wrapper folders (80-200 MB) Pattern 2: ModelURLs.txt links file (1 KB) Pattern 3: Single 2D model in nested zip (5-15 GB) Pattern 4: Compound HMS + RAS in nested zips (8+ GB)
See feature_dev_notes/eBFE_Integration/RESEARCH_FINDINGS.md for complete pattern documentation.
File Classification Rules
HMS Model/ Folder
Include files with these extensions or patterns:
.hms- HEC-HMS project.basin- Basin model file.met- Meteorology file.control- Control specifications.run- Run configuration.results- HMS results.dssinHydrology/path.log,.outin HMS folders
Path indicators: Hydrology/, HMS/
RAS Model/ Folder
Include files with these extensions or patterns:
.prj- HEC-RAS project (validate: contains "Proj Title=", "Geom File=").g##- Geometry file.p##- Plan file.f##- Flow file.u##- Unsteady flow file.c##- Sediment file.b##- Bridge/culvert file.bco##- Boundary condition override.IC.O##- Initial conditions override.x##- Cross section index.rasmap- RAS Mapper project.dsc- DSS catalog (if in RAS folder).dss(if in RAS folder, not HMS folder)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 560 lines · 110 tokens per session scan A 940d63b00a1d
ebfe_organize_models is a skill published in the GitHub repository gpt-cmdr/ras-commander (79 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 4,595 once invoked, about $0.0006 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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