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 wentorai/research-plugins --skill seismology-data-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/seismology-data-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/seismology-data-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/seismology-data-guide/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/wentorai/research-plugins/seismology-data-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/seismology-data-guide.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 | $0.00019 | $0.01839 |
| Opus 5 | $0.00010 | $0.00920 |
| Sonnet 5 | $0.00004 | $0.00368 |
| Haiku 4.5 | $0.00002 | $0.00184 |
Grade A, and why
seismology-data-guide 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 6d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Seismology Data Guide
A skill for processing seismic data, analyzing earthquake catalogs, and working with seismograms using standard tools in observational seismology. Covers data retrieval from global networks, waveform processing with ObsPy, magnitude estimation, focal mechanism analysis, and seismic hazard assessment.
Seismic Data Sources
Global Data Centers
| Data Center | Abbreviation | Coverage | Access |
|---|---|---|---|
| IRIS Data Management Center | IRIS DMC | Global broadband | FDSN Web Services |
| European Integrated Data Archive | EIDA | European networks | FDSN Web Services |
| USGS Earthquake Hazards Program | USGS EHP | Global catalog | API + ComCat |
| International Seismological Centre | ISC | Global bulletin | ISC web services |
| NIED F-net | F-net | Japan broadband | NIED website |
Retrieving Earthquake Catalogs
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
client = Client("IRIS")
# Fetch earthquake catalog for a region and time window
catalog = client.get_events(
starttime=UTCDateTime("2024-01-01"),
endtime=UTCDateTime("2024-12-31"),
minmagnitude=5.0,
maxmagnitude=9.0,
minlatitude=30.0, maxlatitude=45.0,
minlongitude=125.0, maxlongitude=150.0,
orderby="magnitude",
)
print(f"Found {len(catalog)} events")
for event in catalog[:5]:
origin = event.preferred_origin()
mag = event.preferred_magnitude()
print(f" M{mag.mag:.1f} {origin.time} "
f"({origin.latitude:.2f}, {origin.longitude:.2f}) "
f"depth={origin.depth/1000:.1f} km")
Waveform Processing
Retrieving and Preprocessing Seismograms
from obspy import UTCDateTime
from obspy.clients.fdsn import Client
client = Client("IRIS")
# Download waveform data for a specific event
t = UTCDateTime("2024-01-01T07:10:00")
st = client.get_waveforms(
network="IU", station="ANMO", location="00", channel="BHZ",
starttime=t, endtime=t + 600, # 10 minutes of data
)
# Standard preprocessing pipeline
st.detrend("demean") # Remove mean
st.detrend("linear") # Remove linear trend
st.taper(max_percentage=0.05, type="cosine") # Taper edges
st.filter("bandpass", freqmin=0.01, freqmax=5.0, corners=4)
# Remove instrument response to get ground velocity (m/s)
inv = client.get_stations(
network="IU", station="ANMO", location="00", channel="BHZ",
starttime=t, endtime=t + 600, level="response",
)
st.remove_response(inventory=inv, output="VEL", pre_filt=[0.005, 0.01, 8, 10])
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.
- 6d ago First seen · 209 lines · 19 tokens per session scan A c6f0dc49ea28
seismology-data-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,839 once invoked, about $0.0001 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 2026-09-03.
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