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/precip_analyze_aorc/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/precip_analyze_aorc)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/precip_analyze_aorc"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/precip_analyze_aorc/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/precip_analyze_aorc"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/precip_analyze_aorc.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 | $0.00160 | $0.03190 |
| Opus 5 | $0.00080 | $0.01595 |
| Sonnet 5 | $0.00032 | $0.00638 |
| Haiku 4.5 | $0.00016 | $0.00319 |
Grade A, and why
precip_analyze_aorc 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 12d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing AORC Precipitation
Purpose: Navigate precipitation workflows for HEC-RAS/HMS models using AORC historical data and Atlas 14 design storms.
This skill is a NAVIGATOR -- read the primary sources below for complete workflows and API documentation. Do not duplicate implementation details here.
Primary Sources (Read These First!)
1. Canonical Precipitation Contract
ras_commander/precip/AGENTS.md - canonical local contract
Contains:
- Method selection for AORC, Atlas 14, HRRR, and gridded-met workflows
- Critical precipitation rules
- Validation expectations
- Reference notebooks for working examples
Use source docstrings for method signatures and parameter details.
2. AORC Demonstration Notebook
examples/900_aorc_precipitation.ipynb
Live working example showing:
- AORC data retrieval from cloud storage
- Spatial averaging over watersheds
- Temporal aggregation to HEC-RAS intervals
- Export to DSS and CSV formats
- Integration with HEC-RAS unsteady flow files
3. Atlas 14 Single-Project Workflow
examples/720_atlas14_aep_events.ipynb
Complete design storm workflow:
- Query Atlas 14 precipitation frequency values
- Generate SCS Type II temporal distributions
- Apply areal reduction factors
- Create HEC-RAS plans for multiple AEP events
- Batch execution and results processing
4. Atlas 14 Multi-Project Batch Processing
examples/722_atlas14_multi_project.ipynb
Advanced batch processing:
- Process multiple HEC-RAS projects simultaneously
- Standardized AEP suite (10%, 2%, 1%, 0.2%)
- Automated plan creation across projects
- Parallel execution with result consolidation
Quick Start
AORC Historical Data (30 seconds)
from ras_commander.precip import PrecipAorc
# Retrieve hourly AORC data for watershed
aorc_data = PrecipAorc.retrieve_aorc_data(
watershed="02070010", # HUC-8 code or shapefile path
start_date="2015-05-01",
end_date="2015-05-15"
)
# Spatial average over watershed
avg_precip = PrecipAorc.spatial_average(aorc_data, watershed)
# Aggregate to HEC-RAS interval
hourly = PrecipAorc.aggregate_to_interval(avg_precip, interval="1HR")
# Export to DSS for HEC-RAS
PrecipAorc.export_to_dss(
hourly,
dss_file="precipitation.dss",
pathname="/PROJECT/PRECIP/AORC//1HOUR/OBS/"
)
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.
- 12d ago First seen · 368 lines · 160 tokens per session scan A 7761233844f1
precip_analyze_aorc is a skill published in the GitHub repository gpt-cmdr/ras-commander (79 stars, last pushed today), licensed MIT. It adds 160 tokens to every session and 3,190 once invoked, about $0.0008 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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