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landlablandlab 命令行

Agent Skill

landlab 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:landlab(landlab 命令行)
来源仓库:https://github.com/steadfastasart/geoscience-skills
仓库路径:skills/landlab
安装命令:
npx skills add https://github.com/steadfastasart/geoscience-skills --skill landlab
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/steadfastasart/geoscience-skills --skill landlab

简介

landlab 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它可结合来源仓库、安装命令和原始 README 继续核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 当前顶部介绍为空,需参考原始 SKILL.md 补充细节。
  • landlab 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Landlab - Surface Process Modelling

Quick Reference

from landlab import RasterModelGrid
from landlab.components import FlowAccumulator, StreamPowerEroder
import numpy as np

# Create grid
grid = RasterModelGrid((100, 100), xy_spacing=10.0)
z = grid.add_zeros('topographic__elevation', at='node')
z += np.random.rand(grid.number_of_nodes) * 0.1

# Set boundaries (open bottom edge)
grid.set_closed_boundaries_at_grid_edges(True, True, True, False)

# Create and run components
fa = FlowAccumulator(grid, flow_director='D8')
sp = StreamPowerEroder(grid, K_sp=1e-5)

for _ in range(100):
    fa.run_one_step()
    sp.run_one_step(dt=1000)
    z[grid.core_nodes] += 0.001 * 1000  # Uplift

grid.imshow('topographic__elevation', cmap='terrain')

Grid Types

GridUse Case
RasterModelGridRegular rectangular grids (most common)
HexModelGridHexagonal grids (isotropic flow)
VoronoiDelaunayGridIrregular point distributions
NetworkModelGridChannel networks only

Key Concepts

Fields and Boundaries

# Fields: data stored at grid elements (nodes, links, cells)
z = grid.add_zeros('topographic__elevation', at='node')
grid.at_node['drainage_area']  # Access existing field

# Boundaries: close all edges except outlet
grid.set_closed_boundaries_at_grid_edges(True, True, True, False)
z[grid.core_nodes] += uplift * dt  # Core nodes exclude boundaries

Essential Operations

Flow Routing

from landlab.components import FlowAccumulator

fa = FlowAccumulator(grid, flow_director='D8')  # or 'Steepest', 'MFD'
fa.run_one_step()
drainage_area = grid.at_node['drainage_area']

Stream Power Erosion

from landlab.components import StreamPowerEroder

sp = StreamPowerEroder(grid, K_sp=1e-5, m_sp=0.5, n_sp=1.0)
sp.run_one_step(dt=1000)  # dt in years

Hillslope Diffusion

from landlab.components import LinearDiffuser

ld = LinearDiffuser(grid, linear_diffusivity=0.01)  # m^2/yr
ld.run_one_step(dt=100)

Load/Save DEM Data

from landlab.io import read_esri_ascii, write_esri_ascii
from landlab.io.netcdf import read_netcdf, write_netcdf

grid, z = read_esri_ascii('dem.asc', name='topographic__elevation')
write_esri_ascii('output.asc', grid, names='topographic__elevation')
write_netcdf('output.nc', grid)  # Save all fields

Multi-Component Model

from landlab import RasterModelGrid
from landlab.components import FlowAccumulator, StreamPowerEroder, LinearDiffuser

grid = RasterModelGrid((100, 100), xy_spacing=100.0)
z = grid.add_zeros('topographic__elevation', at='node')
z += grid.node_y / 1000 + np.random.rand(grid.number_of_nodes) * 0.1
grid.set_closed_boundaries_at_grid_edges(True, True, True, False)

fa = FlowAccumulator(grid, flow_director='D8')
sp = StreamPowerEroder(grid, K_sp=1e-5)
ld = LinearDiffuser(grid, linear_diffusivity=0.01)

dt, uplift_rate = 1000, 0.001
for _ in range(500):
    fa.run_one_step()
    sp.run_one_step(dt)
    ld.run_one_step(dt)
    z[grid.core_nodes] += uplift_rate * dt

When to Use vs Alternatives

Use CaseToolWhy
Landscape evolution modellingLandlabModular components, Python-native
Basin-scale stratigraphyBadlandsFocus on sediment deposition and basin fill
Topographic analysis (MATLAB)TopoToolboxMature MATLAB toolkit for DEM analysis
Simple diffusion/erosionCustom numpyFewer dependencies for basic models
Coupled surface-subsurfaceLandlabComponents for hydrology + geomorphology
Channel network extractionLandlab or pyshedsBoth handle flow routing well
Soil production and transportLandlabDedicated weathering and soil components
Teaching geomorphologyLandlabClear component API, good tutorials

Choose Landlab when: You need a modular, component-based framework for landscape evolution modelling that combines multiple surface processes (erosion, diffusion, flow routing, weathering) in a single simulation.

Choose Badlands when: Your focus is on basin-scale landscape evolution with emphasis on sediment transport and stratigraphic architecture.

Choose custom numpy when: You only need a simple 2D diffusion or stream power model without the overhead of a full component framework.

Common Workflows

Landscape Evolution Model with Erosion and Uplift

  • Create RasterModelGrid with appropriate dimensions and spacing
  • Initialize topographic__elevation field (flat + noise, or load DEM)
  • Set boundary conditions (open one edge as outlet, close others)
  • Create FlowAccumulator with chosen flow director (D8 or MFD)
  • Create StreamPowerEroder with erosion coefficient K_sp
  • Create LinearDiffuser for hillslope processes
  • Define time step dt and total runtime; choose uplift rate
  • Run time loop: flow routing, erosion, diffusion, then uplift
  • Save snapshots at intervals with write_netcdf() or write_esri_ascii()
  • Visualize final topography with grid.imshow()
  • Analyze drainage area and channel profiles
  • Extract river long profiles for steepness analysis

Common Issues

IssueSolution
Flat areas block flowAdd small random noise to initial topography
Boundary effectsEnsure at least one open boundary edge for drainage
Unstable erosionReduce dt or K_sp; check Courant condition
Wrong field nameUse exact Landlab names: 'topographic__elevation', 'drainage_area'
Memory with large gridsReduce grid resolution or use NetworkModelGrid for channels only

Tips

  1. Set boundaries first - before adding components
  2. Use core_nodes - excludes boundary nodes for operations
  3. Check field names - components expect specific names (e.g., 'topographic__elevation')
  4. Start simple - add components incrementally and verify each

References

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

39.15%
按下载量换算33

Claude

28.27%
按下载量换算24

Cursor

19.49%
按下载量换算17

Gemini CLI

8.9%
按下载量换算8

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安装前确认

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