using-geoscience-skills

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Using Geoscience Skills

使用地球科学技能

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.

用于发现、路由和组合地球科学技能库的元技能。该技能将用户意图映射到领域技能、工作流技能、斜杠命令和Agent。

Domain Routing Table

领域路由表

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

将用户意图关键词匹配到对应的领域技能。

关键词/触发词	技能	领域
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

工作流技能

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

工作流技能将多个领域技能链接成端到端的流水线。

工作流	斜杠命令	技能链
地震解释	`/seismic-workflow`	segyio -> obspy -> bruges -> disba -> pyvista
测井评价	`/well-analysis`	lasio/dlisio -> welly -> petropy -> striplog -> pyvista
地质建模	`/model-3d`	gemgis -> gempy/loopstructural -> pyvista
地球物理反演	`/inversion-workflow`	simpeg/pygimli -> verde -> pyvista
岩石物理与AVO	`/rock-physics`	lasio/welly -> bruges -> segyio

Available Agents

可用Agent

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"

Agent	用途	典型触发词
`data-qc-reviewer`	跨格式自动数据质量检查	"QC my data", "check data quality"
`geoscience-mentor`	地球科学概念与方法的引导式讲解	"explain", "teach me", "what is"

All 30 Domain Skills by Category

按分类划分的全部30项领域技能

Seismic and Seismology

地震与地震学

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

```
segyio
```
-- SEG-Y文件I/O、道与道头访问
```
obspy
```
-- 地震波形处理、FDSN服务
```
disba
```
-- 面波频散（瑞利波、勒夫波）

Well Log Analysis

测井分析

```
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

```
lasio
```
-- LAS文件读写
```
dlisio
```
-- DLIS/RP66二进制测井数据解析
```
welly
```
-- 测井数据分析、曲线QC、多井项目
```
petropy
```
-- 岩石物理分析、地层评价
```
striplog
```
-- 岩性与地层测井展示

3D Geological Modelling

3D地质建模

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

```
gempy
```
-- 隐式3D地质建模
```
loopstructural
```
-- 支持褶皱与断层的3D建模
```
gemgis
```
-- GemPy的空间数据预处理

Geophysical Inversion

地球物理反演

```
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

```
simpeg
```
-- 多方法地球物理反演框架
```
pygimli
```
-- 具有简洁API的ERT、SRT、IP反演
```
devito
```
-- 用于波传播的符号PDE求解器
```
pylops
```
-- 反问题的线性算子

Potential Fields and Rock Physics

位场与岩石物理

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

```
harmonica
```
-- 重力与磁数据处理
```
bruges
```
-- AVO、Gassmann方程、子波、弹性模量

Spatial Analysis and Geostatistics

空间分析与地质统计学

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

```
verde
```
-- 空间网格化与插值
```
geostatspy
```
-- 变异函数、克里金法（GSLIB风格）
```
scikit-gstat
```
-- 基于scikit-learn API的地质统计学
```
gnnwr
```
-- 地理加权神经网络回归

Hydrology and Surface Processes

水文与地表过程

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

```
pastas
```
-- 地下水时间序列建模
```
landlab
```
-- 地貌演化建模

Structural Geology and Geochemistry

构造地质与地球化学

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

```
mplstereonet
```
-- 定向数据的赤平投影图绘制
```
pyrolite
```
-- 地球化学分析与图表

Near-Surface Geophysics

近地表地球物理

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

```
gprpy
```
-- GPR数据处理
```
mtpy
```
-- 大地电磁数据分析

Data Formats and Visualization

数据格式与可视化

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

```
xarray
```
-- NetCDF、多维标记数组
```
pyvista
```
-- 3D网格可视化与分析
```
pooch
```
-- 数据文件获取与缓存

Skill Composition Rules

技能组合规则

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

当任务涉及地球科学工作流的多个阶段时，链接多个技能。

Composition Patterns

组合模式

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)

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

何时链接技能 vs 单独使用技能

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

场景	方法
单一文件格式相关问题	使用独立领域技能
端到端分析流水线	使用工作流技能编排
跨格式数据QC	使用 `data-qc-reviewer` Agent
概念讲解	使用 `geoscience-mentor` Agent
多库代码生成	按顺序链接领域技能

Dependency Awareness

依赖关系注意事项

When composing skills, respect data flow:

python

undefined

组合技能时，需遵循数据流：

python

undefined

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)

undefined

Rpp = zoeppritz(vp1, vs1, rho1, vp2, vs2, rho2, theta)

undefined

When to Use This Skill

何时使用本技能

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.

这是一项发现与路由技能。在以下场景使用：

开启新的地球科学编码会话，不确定使用哪个库
用户请求涉及多个地球科学领域
需要为任务找到合适的斜杠命令或工作流
将多个领域技能组合成流水线
查询哪个技能处理特定文件格式或分析类型

本技能本身不执行任何分析，它会引导至合适的领域技能、工作流技能或Agent进行执行。

Quick Decision Tree

快速决策树

text

User wants to...
  |
  +-- Load/write a file? --> Check format:
  |     SEG-Y -> segyio    LAS -> lasio    DLIS -> dlisio
  |     NetCDF -> xarray    VTK -> pyvista
  |
  +-- Process signals? --> obspy (seismology), welly (well logs)
  |
  +-- Build a model?
  |     3D geology -> gempy or loopstructural
  |     Rock physics -> bruges
  |     Inversion -> simpeg or pygimli
  |
  +-- Visualize results? --> pyvista (3D), matplotlib (2D via domain skill)
  |
  +-- Run a full pipeline? --> Use workflow skills above

text

用户想要...
  |
  +-- 加载/写入文件？ --> 检查格式：
  |     SEG-Y -> segyio    LAS -> lasio    DLIS -> dlisio
  |     NetCDF -> xarray    VTK -> pyvista
  |
  +-- 处理信号？ --> obspy（地震学）, welly（测井）
  |
  +-- 构建模型？
  |     3D地质 -> gempy 或 loopstructural
  |     岩石物理 -> bruges
  |     反演 -> simpeg 或 pygimli
  |
  +-- 可视化结果？ --> pyvista（3D）, matplotlib（通过领域技能实现2D）
  |
  +-- 运行完整流水线？ --> 使用上述工作流技能