Agent skill · Data & Analytics

obspy

Seismology data processing with ObsPy. Helps with reading seismic waveforms, filtering/processing time series, fetching data from FDSN services, and earthquake analysis. Use when Claude needs to: (1) Read seismic data formats (MiniSEED, SAC, GSE2, SEGY), (2) Filter or process waveforms, (3) Fetch data from IRIS/USGS/FDSN services, (4) Search for earthquakes by magnitude/location, (5) Plot seismograms or spectrograms, (6) Remove instrument response, (7) Analyze station metadata.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill obspy --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Version: 1.0.0
Declared author: Geoscience Skills
Requires: [obspy>=1.4.0]
Path: skills/analysis/obspy/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# ObsPy - Seismology Data Processing ## Quick Reference ```python from obspy import read, UTCDateTime from obspy.clients.fdsn import Client # Read local file (MiniSEED, SAC, etc.) st = read("data.mseed") tr = st[0] # First trace print(tr.stats) # Metadata # Fetch from FDSN client = Client("IRIS") t = UTCDateTime("2023-02-06T01:17:00") st = client.get_waveforms("IU", "ANMO", "00", "LHZ", t, t + 3600) st.plot() ``` ## Key Classes | Class | Purpose | |-------|---------| | `Stream` | Container for multiple Trace objects | | `Trace` | Single waveform with data + metadata | | `UTCDateTime` | Precise time handling | | `Inventory` | Station/channel metadata | | `Catalog` | Earthquake event information | ## Essential Operations ### Read and Inspect ```python st = read("data.mseed") # Auto-detect format tr = st[0] print(tr.stats.station, tr.stats.channel, tr.stats.sampling_rate) ``` ### Filter and Process ```python st.detrend("demean") # Remove mean st.detrend("linear") # Remove trend st.taper(max_percentage=0.05) # Taper edges st.filter("bandpass", freqmin=0.1, freqmax=10.0) ``` ### Fetch Waveforms from FDSN ```python client = Client("IRIS") t1 = UTCDateTime("2023-02-06T01:17:00") st = clie

What's inside
Steps it walks through
  1. Quick Reference
  2. Key Classes
  3. Essential Operations
  4. Read and Inspect
  5. Filter and Process
  6. Fetch Waveforms from FDSN
  7. Search Earthquakes
  8. Get Station Metadata
  9. Remove Instrument Response
  10. Trim and Select
  11. Merge and Handle Gaps
  12. Writing Data
  13. Error Handling
  14. Common Tips
Ships with 1 file
  • metadata.json
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About this skill
What does the obspy skill do?

Seismology data processing with ObsPy. Helps with reading seismic waveforms, filtering/processing time series, fetching data from FDSN services, and earthquake analysis. Use when Claude needs to: (1) Read seismic data formats (MiniSEED, SAC, GSE2, SEGY), (2) Filter or process waveforms, (3) Fetch data from IRIS/USGS/FDSN services, (4) Search for earthquakes by magnitude/location, (5) Plot seismograms or spectrograms, (6) Remove instrument response, (7) Analyze station metadata.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill obspy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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