Using Geoscience Skills

Meta-skill for discovering, routing, and composing the geoscience skills library. This skill maps user intent to domain skills, workflow skills, slash commands, and agents.

Domain Routing Table

Match user intent keywords to the appropriate domain skill.

Keywords / Triggers	Skill	Domain
SEG-Y, seismic traces, trace headers, inline, crossline	`segyio`	Seismic I/O
waveform, earthquake, FDSN, seismogram, miniSEED	`obspy`	Seismology
surface wave, dispersion, Rayleigh, Love wave	`disba`	Seismology
LAS, well logs, wireline, borehole curves	`lasio`	Well Logs
DLIS, RP66, array logs, modern well data	`dlisio`	Well Logs
well analysis, curve QC, multi-well, despike	`welly`	Well Logs
petrophysics, Sw, porosity, formation evaluation	`petropy`	Petrophysics
lithology, stratigraphy, striplog, facies log	`striplog`	Stratigraphy
3D model, geology, implicit surface, faults	`gempy`	3D Modelling
fold modelling, structural frame, Loop3D	`loopstructural`	3D Modelling
GIS, spatial data prep, borehole to GemPy	`gemgis`	GIS Preprocessing
inversion, DC resistivity, magnetics, gravity, EM	`simpeg`	Inversion
ERT, SRT, IP, near-surface inversion	`pygimli`	Inversion
PDE, wave equation, finite differences, stencil	`devito`	Simulation
linear operator, inverse problem, sparsity	`pylops`	Inverse Problems
gravity, magnetic, Bouguer, upward continuation	`harmonica`	Potential Fields
AVO, Zoeppritz, Gassmann, fluid substitution, wavelet	`bruges`	Rock Physics
gridding, interpolation, spatial, Verde	`verde`	Spatial Analysis
variogram, kriging, GSLIB, geostatistics	`geostatspy`	Geostatistics
variogram fitting, scikit-learn style geostat	`scikit-gstat`	Geostatistics
spatial regression, GWR, GNNWR, non-stationarity, coefficient mapping	`gnnwr`	Spatial Regression
groundwater, time series, pumping test	`pastas`	Hydrology
landscape, erosion, surface processes, DEM	`landlab`	Surface Processes
stereonet, strike, dip, poles, structural	`mplstereonet`	Structural Geology
geochemistry, REE, spider diagram, ternary	`pyrolite`	Geochemistry
GPR, ground-penetrating radar, radargram	`gprpy`	Near-Surface
magnetotellurics, MT, impedance tensor	`mtpy`	Near-Surface
NetCDF, xarray, multi-dimensional, climate	`xarray`	Data Formats
3D visualization, mesh, VTK, point cloud	`pyvista`	Visualization
data download, sample data, cache, fetch	`pooch`	Utilities

Workflow Skills

Workflow skills chain multiple domain skills into end-to-end pipelines.

Workflow	Slash Command	Skill Chain
Seismic Interpretation	`/seismic-workflow`	segyio -> obspy -> bruges -> disba -> pyvista
Well Log Evaluation	`/well-analysis`	lasio/dlisio -> welly -> petropy -> striplog -> pyvista
Geological Modelling	`/model-3d`	gemgis -> gempy/loopstructural -> pyvista
Geophysical Inversion	`/inversion-workflow`	simpeg/pygimli -> verde -> pyvista
Rock Physics & AVO	`/rock-physics`	lasio/welly -> bruges -> segyio

Available Agents

Agent	Purpose	Typical Trigger
`data-qc-reviewer`	Automated data quality checks across formats	"QC my data", "check data quality"
`geoscience-mentor`	Guided explanations of geoscience concepts and methods	"explain", "teach me", "what is"

All 30 Domain Skills by Category

Seismic and Seismology

```
segyio
```
-- SEG-Y file I/O, trace and header access
```
obspy
```
-- seismological waveform processing, FDSN services
```
disba
```
-- surface wave dispersion (Rayleigh, Love)

Well Log Analysis

```
lasio
```
-- LAS file reading and writing
```
dlisio
```
-- DLIS/RP66 binary well log parsing
```
welly
```
-- well data analysis, curve QC, multi-well projects
```
petropy
```
-- petrophysical analysis, formation evaluation
```
striplog
```
-- lithological and stratigraphic log display

3D Geological Modelling

```
gempy
```
-- implicit 3D geological modelling
```
loopstructural
```
-- 3D modelling with fold and fault support
```
gemgis
```
-- spatial data preprocessing for GemPy

Geophysical Inversion

```
simpeg
```
-- multi-method geophysical inversion framework
```
pygimli
```
-- ERT, SRT, IP inversion with simple API
```
devito
```
-- symbolic PDE solver for wave propagation
```
pylops
```
-- linear operators for inverse problems

Potential Fields and Rock Physics

```
harmonica
```
-- gravity and magnetic data processing
```
bruges
```
-- AVO, Gassmann, wavelets, elastic moduli

Spatial Analysis and Geostatistics

```
verde
```
-- spatial gridding and interpolation
```
geostatspy
```
-- variograms, kriging (GSLIB-style)
```
scikit-gstat
```
-- geostatistics with scikit-learn API
```
gnnwr
```
-- geographically weighted neural network regression

Hydrology and Surface Processes

```
pastas
```
-- groundwater time series modelling
```
landlab
```
-- landscape evolution modelling

Structural Geology and Geochemistry

```
mplstereonet
```
-- stereonet plots for orientation data
```
pyrolite
```
-- geochemical analysis and diagrams

Near-Surface Geophysics

```
gprpy
```
-- GPR data processing
```
mtpy
```
-- magnetotelluric data analysis

Data Formats and Visualization

```
xarray
```
-- NetCDF, multi-dimensional labeled arrays
```
pyvista
```
-- 3D mesh visualization and analysis
```
pooch
```
-- data file fetching and caching

Skill Composition Rules

Chain skills when a task spans multiple stages of a geoscience workflow.

Composition Patterns

text

Data Loading -> Processing -> Modelling -> Visualization

1. Always start with a data I/O skill (segyio, lasio, dlisio, xarray)
2. Use processing skills for QC and transformation (welly, obspy, verde)
3. Apply domain modelling (bruges, gempy, simpeg, pygimli)
4. Finish with visualization (pyvista, matplotlib via domain skill)

When to Chain vs Use Standalone

Scenario	Approach
Single file format question	Standalone domain skill
End-to-end analysis pipeline	Workflow skill to orchestrate
Data QC across formats	`data-qc-reviewer` agent
Concept explanation	`geoscience-mentor` agent
Multi-library code generation	Chain domain skills in order

Dependency Awareness

When composing skills, respect data flow:

python

# Correct: segyio loads, obspy processes, bruges models
import segyio
import obspy
from bruges.reflection import zoeppritz

# Load with segyio
with segyio.open('seismic.sgy') as f:
    data = f.trace[:]

# Process with obspy (convert to Stream if needed)
# Model with bruges
Rpp = zoeppritz(vp1, vs1, rho1, vp2, vs2, rho2, theta)

When to Use This Skill

This is the discovery and routing skill. Use it when:

Starting a new geoscience coding session and unsure which library to use
A user request spans multiple geoscience domains
You need to find the right slash command or workflow for a task
Composing multiple domain skills into a pipeline
Looking up which skill handles a specific file format or analysis type

This skill does not perform any analysis itself. It directs to the appropriate domain skill, workflow skill, or agent for execution.

Quick Decision Tree