flood-detection

flood-detection is a skill for Claude Code, Codex from xuansenpa1/skillrevise. It costs 40 tokens per session (825 once invoked), scanned A, a copy of flood-detection, MIT.

A guide to detecting flood events by comparing measured water levels with flood-stage thresholds. It explains how to turn frequent USGS readings into daily maximums and count days that reached flood stage.

In plain words
What is it for?
Use it to calculate daily peak water levels, compare them with National Weather Service thresholds, count flood days, and analyse flood severity.
Why use it?
It provides a consistent way to identify flooding and classify its severity instead of relying on averages that may hide short-lived peaks.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to calculate daily peak water levels, compare them with National Weather Service thresholds, count flood days, and analyse flood severity.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xuansenpa1/skillrevise/flood-detection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add xuansenpa1/skillrevise --skill flood-detection
Clone the repo
git clone --depth 1 https://github.com/xuansenpa1/skillrevise

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for flood-detection

README.md
[![agentmods](https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/flood-detection/github.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/flood-detection)
Your own site
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/flood-detection"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/flood-detection/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.

agentmods 80×15 button for flood-detection

Your own site · 80×15
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/flood-detection"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/flood-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 825 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00825
Opus 5 $0.00020 $0.00413
Sonnet 5 $0.00008 $0.00165
Haiku 4.5 $0.00004 $0.00082

Measured 8d ago against content hash 14e55948af7a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

flood-detection 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 8d 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.

Origin

This is a copy

100% identical to flood-detection — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skillsbench/tasks/flood-risk-analysis/environment/skills/flood-detection/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Flood Detection Guide

Overview

Flood detection involves comparing observed water levels against established flood stage thresholds. This guide covers how to process water level data and identify flood events.

Flood Stage Definition

According to the National Weather Service, flood stage is the water level at which overflow of the natural banks begins to cause damage. A flood event occurs when:

water_level >= flood_stage_threshold

Aggregating Instantaneous Data to Daily

USGS instantaneous data is recorded at ~15-minute intervals. For flood detection, aggregate to daily maximum:

# df is DataFrame from nwis.get_iv() with datetime index
# gage_col is the column name containing water levels

daily_max = df[gage_col].resample('D').max()

Why Daily Maximum?

Aggregation Use Case
max() Flood detection - captures peak water level
mean() Long-term trends - may miss short flood peaks
min() Low flow analysis

Detecting Flood Days

Compare daily maximum water level against flood threshold:

flood_threshold = <threshold_from_nws>  # feet

# Count days with flooding
flood_days = (daily_max >= flood_threshold).sum()

# Get specific dates with flooding
flood_dates = daily_max[daily_max >= flood_threshold].index.tolist()

Processing Multiple Stations

flood_results = []

for site_id, site_data in all_data.items():
    daily_max = site_data['water_levels'].resample('D').max()
    threshold = thresholds[site_id]['flood']

    days_above = int((daily_max >= threshold).sum())

    if days_above > 0:
        flood_results.append({
            'station_id': site_id,
            'flood_days': days_above
        })

# Sort by flood days descending
flood_results.sort(key=lambda x: x['flood_days'], reverse=True)

Flood Severity Classification

If multiple threshold levels are available:

def classify_flood(water_level, thresholds):
    if water_level >= thresholds['major']:
        return 'major'
    elif water_level >= thresholds['moderate']:
        return 'moderate'
    elif water_level >= thresholds['flood']:
        return 'minor'
    elif water_level >= thresholds['action']:
        return 'action'
    else:
        return 'normal'

Read the full file on GitHub · 128 lines

Changes

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.

  1. 8d ago First seen · 128 lines · 40 tokens per session scan A 14e55948af7a

Subscribe to this mod's changes

flood-detection is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 40 tokens to every session and 825 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to flood-detection, differing in 0 lines, and is treated as a copy.

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