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.
npx skills add OpenLAIR/OpenSkill --skill evo-usgs-flood-detectiongit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-usgs-flood-detection)<a href="https://agentmods.dev/skills/openlair/openskill/evo-usgs-flood-detection"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-usgs-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.
<a href="https://agentmods.dev/skills/openlair/openskill/evo-usgs-flood-detection"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-usgs-flood-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.00719 |
| Opus 5 | $0.00028 | $0.00360 |
| Sonnet 5 | $0.00011 | $0.00144 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
evo-usgs-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 yesterday.
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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evo-usgs-flood-detection
Overview
End-to-end pipeline for detecting flooding at USGS streamgages. Reads station IDs from a file, fetches daily streamflow values and historical statistics via dataretrieval.nwis, computes flood-day counts by comparing observed daily mean streamflow against the 90th percentile historical threshold for each calendar day, and writes filtered results to CSV.
Key Concepts
Flood Definition
A flood day is defined as a day where the observed daily mean streamflow (00060_Mean) exceeds the 90th percentile (p90_va) of historical daily mean streamflow for that same calendar day (month + day).
Data Sources
- Daily Values:
nwis.get_dv(sites, parameterCd='00060', start, end)→ returns(DataFrame, metadata)tuple - Historical Stats:
nwis.get_stats(sites, statReportType='daily', parameterCd='00060')→ returns(DataFrame, metadata)tuple with columnsmonth_nu,day_nu,p90_va
Station ID Handling
USGS station IDs have leading zeros (e.g., 04193500). Always read as strings, never as integers.
Functions
load_stations(filepath: str) -> List[str]
Reads station IDs from a flat text file (one per line) or CSV with station_id header. Preserves leading zeros.
get_station_flood_thresholds(site_id: str) -> pd.DataFrame
Fetches 90th percentile historical daily streamflow thresholds. Returns DataFrame with month_nu, day_nu, p90_va.
detect_floods_for_site(site_id: str, start_date: str, end_date: str) -> int
Fetches daily values, merges with thresholds on month/day, counts days where observed > p90. Returns integer count.
run_flood_detection_pipeline(stations_file, out_file, start, end) -> pd.DataFrame
Orchestrates the full pipeline. Processes all stations, filters to those with >=1 flood day, writes CSV.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-usgs-flood-detection/scripts')
from utils import load_stations, detect_floods_for_site, run_flood_detection_pipeline
# Run full pipeline
results = run_flood_detection_pipeline(
stations_file='/root/data/michigan_stations.txt',
out_file='/root/output/flood_results.csv',
start='2025-04-01',
end='2025-04-07'
)
What ships with it
2 files 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.
- yesterday First seen · 64 lines · 55 tokens per session scan A 066e8732eeea
evo-usgs-flood-detection is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 719 once invoked, about $0.0003 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 2026-09-11.
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