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wellywelly 命令行

Agent Skill

welly 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。

总安装

374

周安装

15

GitHub Stars

23

下载量

121
CodexClaudeCursorGemini CLI

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

简介

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 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定仓库安装并使用。
  • 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。
  • 可结合来源仓库和原始 README 进一步核验具体用法。

SKILL.md

welly - Well Data Analysis

Quick Reference

from welly import Well, Project

# Load single well
w = Well.from_las('well.las')

# Access data
df = w.df()                      # DataFrame
gr = w.data['GR']                # Curve object
values = gr.values               # numpy array
depth = gr.basis                 # depth array

# Well info
print(w.name, w.uwi)
print(w.data.keys())             # Available curves

# Load multiple wells
p = Project.from_las('wells/*.las')
for well in p:
    print(well.name)

Key Classes

ClassPurpose
WellSingle well with curves, location, tops
ProjectCollection of wells for multi-well workflows
CurveLog curve with depth basis, units, and processing methods

Essential Operations

Access Curve Data

gr = w.data['GR']
print(gr.mnemonic, gr.units)     # Metadata
print(gr.start, gr.stop, gr.step)  # Depth range

Process Curves

gr = w.data['GR']

# Clean and filter
gr_clean = gr.despike(window=5, z=2)
gr_smooth = gr.smooth(window=11)

# Transform
gr_norm = gr.normalize()         # 0-1 range
gr_resampled = gr.resample(step=0.5)
gr_clipped = gr.clip(top=1500, bottom=2000)

Work with Formation Tops

w.tops = {
    'TopFormationA': 1500.0,
    'TopFormationB': 1750.0,
}

for name, depth in w.tops.items():
    print(f"{name}: {depth} m")

Multi-Well Project

from welly import Project

p = Project.from_las('wells/*.las')
print(f"Loaded {len(p)} wells")

# Filter and analyze
for w in p:
    if 'GR' in w.data:
        print(f"{w.name}: GR mean={w.data['GR'].values.mean():.1f}")

Export Data

# To DataFrame
df = w.df()

# To LAS file
w.to_las('output.las')

# To CSV
df.to_csv('well_data.csv')

Common Curve Mnemonics

MnemonicDescriptionUnits
GRGamma RayGAPI
NPHINeutron Porosityv/v
RHOBBulk Densityg/cc
DTSonicus/ft
RT/ILDDeep Resistivityohm.m
CALICaliperin

Tips

  1. Use Project for multi-well workflows - easier than managing individual files
  2. Check units - welly tracks units, ensure consistency
  3. Despike before analysis - remove outliers with curve.despike()
  4. Resample to common basis - use curve.resample() for cross-well comparison
  5. welly extends lasio - all lasio functionality available

When to Use vs Alternatives

ToolBest For
wellyMulti-well projects, curve processing, formation tops management
lasioLow-level LAS file I/O, header manipulation, malformed files
petropyPetrophysical calculations (Vsh, porosity, Sw, permeability)

Use welly when you need to manage wells as objects with curves, tops, and metadata -- especially for multi-well QC and cross-well analysis via Project.

Use lasio instead when you only need to read/write LAS files, handle malformed headers, or need fine control over LAS formatting.

Use petropy instead when your focus is formation evaluation calculations (shale volume, porosity, water saturation) rather than data management.

Common Workflows

Load and QC a multi-well project

- [ ] Load wells with `Project.from_las('wells/*.las')`
- [ ] Check well count and names: `len(p)`, iterate wells
- [ ] Verify required curves exist in each well (`'GR' in w.data`)
- [ ] Despike and clean noisy curves: `curve.despike()`
- [ ] Resample to common depth basis for cross-well comparison
- [ ] Compute summary statistics per well (mean, min, max)
- [ ] Export cleaned data to LAS or DataFrame

References

Scripts

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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

35.11%
按下载量换算42

Claude

26.99%
按下载量换算33

Cursor

19.44%
按下载量换算24

Gemini CLI

8.93%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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