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devitodevito 搜索

Agent Skill

devito 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

259

周安装

11

GitHub Stars

23

下载量

91
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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:devito(devito 搜索)
来源仓库:https://github.com/steadfastasart/geoscience-skills
仓库路径:skills/devito
安装命令:
npx skills add https://github.com/steadfastasart/geoscience-skills --skill devito
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 devito

简介

用于查找、检索和筛选相关信息,适合在关键词搜索或任务线索驱动下快速定位候选结果。

  • 适用于科研、工程领域的信息收集场景,尤其在 Codex、Claude 等宿主中辅助背景调研。
  • 通过 GitHub 安装,具体用法需结合来源仓库 README 进一步确认。
  • 安装前建议核实权限范围、维护状态,以及是否触发联网或文件读写操作。
  • 注意结果可能与实际业务上下文存在偏差,需人工核验关键信息准确性。

SKILL.md

Devito - Symbolic PDE Solver

Quick Reference

from devito import Grid, Function, TimeFunction, Eq, solve, Operator

# Create grid
grid = Grid(shape=(101, 101), extent=(1000., 1000.))

# Velocity model
v = Function(name='v', grid=grid, space_order=4)
v.data[:] = 1500.

# Wavefield
p = TimeFunction(name='p', grid=grid, time_order=2, space_order=4)

# Wave equation: d2p/dt2 = v^2 * laplacian(p)
stencil = Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward))

# Compile and run
op = Operator([stencil])
op(time_M=100, dt=0.5)

Key Classes

ClassPurpose
GridComputational domain definition
FunctionSpatial field on grid
TimeFunctionTime-dependent field
SparseTimeFunctionPoint sources/receivers
OperatorCompiled computation kernel

Essential Operations

Grid and Fields

from devito import Grid, Function, TimeFunction

# 2D/3D Grid
grid = Grid(shape=(nx, nz), extent=(x_size, z_size))

# Velocity model (spatial field)
v = Function(name='v', grid=grid, space_order=4)
v.data[:] = 1500.

# Wavefield (time-dependent)
p = TimeFunction(name='p', grid=grid, time_order=2, space_order=4)

Source and Receivers

from examples.seismic import RickerSource, Receiver, TimeAxis

time_range = TimeAxis(start=0., stop=1000., step=dt)

# Source
src = RickerSource(name='src', grid=grid, f0=10., npoint=1, time_range=time_range)
src.coordinates.data[0, :] = [500., 20.]

# Receivers
rec = Receiver(name='rec', grid=grid, npoint=101, time_range=time_range)
rec.coordinates.data[:, 0] = np.linspace(0., 1000., 101)
rec.coordinates.data[:, 1] = 20.

Build and Run

# Wave equation
stencil = Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward))
src_term = src.inject(field=p.forward, expr=src * dt**2 * v**2)
rec_term = rec.interpolate(expr=p)

# Compile and execute
op = Operator([stencil] + src_term + rec_term)
op(time_M=nt-1, dt=dt)

# Results
shot_record = rec.data        # (nt, nrec)
snapshot = p.data[0]          # Current wavefield

Symbolic Derivatives

SyntaxDescription
p.dt, p.dt2First/second time derivative
p.dx, p.dy, p.dzSpatial derivatives
p.laplaceLaplacian (auto-adapts to dims)
p.forwardp at t+dt (time stepping)
p.backwardp at t-dt (adjoint)

Stability and Accuracy

CFL Condition: dt < dx / (v_max * sqrt(ndim))

DimsMax dt
2Ddx / (v_max * 1.414)
3Ddx / (v_max * 1.732)
Space OrderStencil PointsError
23O(h^2)
45O(h^4)
89O(h^8)

Higher order = more accurate but slower. Use 4-8 for production.

When to Use vs Alternatives

ScenarioRecommendation
Seismic wave propagation (acoustic/elastic)Devito - symbolic PDE, auto-optimized code
Full Waveform Inversion (FWI) or RTMDevito - adjoint support, GPU-ready
Legacy seismic processing pipelinesMadagascar - established, large script library
Simple 1D/2D wave demosCustom NumPy - no dependencies, easier to debug
General-purpose PDE solving (non-wave)FEniCS - FEM-based, broader PDE support
Production seismic imaging at scaleDevito - generates optimized C code, MPI support

Choose Devito when: You need high-performance finite-difference wave propagation with symbolic equation specification. It auto-generates optimized C/OpenMP/GPU code from Python-level math, making it ideal for FWI, RTM, and research prototyping.

Avoid Devito when: You need finite-element methods (use FEniCS), or simple pedagogical examples where NumPy suffices.

Common Workflows

Acoustic wave forward modelling with sources and receivers

  • Define Grid with shape and physical extent matching the velocity model
  • Create velocity Function and populate with model values
  • Create TimeFunction for the wavefield (time_order=2, space_order=4+)
  • Verify CFL condition: dt < dx / (v_max * sqrt(ndim))
  • Build wave equation stencil: Eq(p.forward, solve(p.dt2 - v**2 * p.laplace, p.forward))
  • Create source (RickerSource) and receivers, set coordinates
  • Add source injection and receiver interpolation terms
  • Compile Operator with stencil + source + receiver terms
  • Run operator: op(time_M=nt-1, dt=dt)
  • Extract shot record from rec.data and plot

References

Scripts

适合场景

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02

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

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

34.26%
按下载量换算31

Claude

29.61%
按下载量换算27

Cursor

20.06%
按下载量换算18

Gemini CLI

10.16%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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