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Agent Skill · 研究检索

astropy(astropy 搜索)

astropy 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。

astropy 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 18,153。

总安装 6,233周安装 265GitHub Stars 26,409下载 2,184核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:astropy(astropy 搜索)
来源仓库:https://github.com/davila7/claude-code-templates
仓库路径:skills/astropy
安装命令:
npx skills add https://github.com/davila7/claude-code-templates --skill astropy
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/davila7/claude-code-templates --skill astropy

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

astropy 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它提供 Python 天文数据处理核心包支持,用于坐标转换、单位计算和 FITS 文件操作。

使用方式是通过 npx skills add 命令安装,需结合 ICRS、Galactic 等坐标系使用。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。astropy 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/astropy

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/davila7/claude-code-templates --skill astropy;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

astropy 怎么安装?

在终端执行 npx skills add https://github.com/davila7/claude-code-templates --skill astropy。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

astropy 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

astropy 需要什么权限?

权限级别是需要联网。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。

astropy 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

astropy 用的人多吗?

公开数据是累计安装 6,233 次,近一周安装 265 次,下载 2,184 次,GitHub 26,409 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

astropy 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 来源仓库有 26,409 星,维护活跃度相对可查。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
astropy

astropy 搜索 · 本页

6,23326,409需要联网MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Astropy

Overview

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis. Use astropy for coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing.

When to Use This Skill

Use astropy when tasks involve:

  • Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)
  • Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)
  • Reading, writing, or manipulating FITS files (images or tables)
  • Cosmological calculations (luminosity distance, lookback time, Hubble parameter)
  • Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)
  • Table operations (reading catalogs, cross-matching, filtering, joining)
  • WCS transformations between pixel and world coordinates
  • Astronomical constants and calculations

Quick Start

import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.time import Time
from astropy.io import fits
from astropy.table import Table
from astropy.cosmology import Planck18

# Units and quantities
distance = 100 * u.pc
distance_km = distance.to(u.km)

# Coordinates
coord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')
coord_galactic = coord.galactic

# Time
t = Time('2023-01-15 12:30:00')
jd = t.jd  # Julian Date

# FITS files
data = fits.getdata('image.fits')
header = fits.getheader('image.fits')

# Tables
table = Table.read('catalog.fits')

# Cosmology
d_L = Planck18.luminosity_distance(z=1.0)

Core Capabilities

1. Units and Quantities (astropy.units)

Handle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.

Key operations:

  • Create quantities by multiplying values with units
  • Convert between units using .to() method
  • Perform arithmetic with automatic unit handling
  • Use equivalencies for domain-specific conversions (spectral, doppler, parallax)
  • Work with logarithmic units (magnitudes, decibels)

See: references/units.md for comprehensive documentation, unit systems, equivalencies, performance optimization, and unit arithmetic.

2. Coordinate Systems (astropy.coordinates)

Represent celestial positions and transform between different coordinate frames.

Key operations:

  • Create coordinates with SkyCoord in any frame (ICRS, Galactic, FK5, AltAz, etc.)
  • Transform between coordinate systems
  • Calculate angular separations and position angles
  • Match coordinates to catalogs
  • Include distance for 3D coordinate operations
  • Handle proper motions and radial velocities
  • Query named objects from online databases

See: references/coordinates.md for detailed coordinate frame descriptions, transformations, observer-dependent frames (AltAz), catalog matching, and performance tips.

3. Cosmological Calculations (astropy.cosmology)

Perform cosmological calculations using standard cosmological models.

Key operations:

  • Use built-in cosmologies (Planck18, WMAP9, etc.)
  • Create custom cosmological models
  • Calculate distances (luminosity, comoving, angular diameter)
  • Compute ages and lookback times
  • Determine Hubble parameter at any redshift
  • Calculate density parameters and volumes
  • Perform inverse calculations (find z for given distance)

See: references/cosmology.md for available models, distance calculations, time calculations, density parameters, and neutrino effects.

4. FITS File Handling (astropy.io.fits)

Read, write, and manipulate FITS (Flexible Image Transport System) files.

Key operations:

  • Open FITS files with context managers
  • Access HDUs (Header Data Units) by index or name
  • Read and modify headers (keywords, comments, history)
  • Work with image data (NumPy arrays)
  • Handle table data (binary and ASCII tables)
  • Create new FITS files (single or multi-extension)
  • Use memory mapping for large files
  • Access remote FITS files (S3, HTTP)

See: references/fits.md for comprehensive file operations, header manipulation, image and table handling, multi-extension files, and performance considerations.

5. Table Operations (astropy.table)

Work with tabular data with support for units, metadata, and various file formats.

Key operations:

  • Create tables from arrays, lists, or dictionaries
  • Read/write tables in multiple formats (FITS, CSV, HDF5, VOTable)
  • Access and modify columns and rows
  • Sort, filter, and index tables
  • Perform database-style operations (join, group, aggregate)
  • Stack and concatenate tables
  • Work with unit-aware columns (QTable)
  • Handle missing data with masking

See: references/tables.md for table creation, I/O operations, data manipulation, sorting, filtering, joins, grouping, and performance tips.

6. Time Handling (astropy.time)

Precise time representation and conversion between time scales and formats.

Key operations:

  • Create Time objects in various formats (ISO, JD, MJD, Unix, etc.)
  • Convert between time scales (UTC, TAI, TT, TDB, etc.)
  • Perform time arithmetic with TimeDelta
  • Calculate sidereal time for observers
  • Compute light travel time corrections (barycentric, heliocentric)
  • Work with time arrays efficiently
  • Handle masked (missing) times

See: references/time.md for time formats, time scales, conversions, arithmetic, observing features, and precision handling.

7. World Coordinate System (astropy.wcs)

Transform between pixel coordinates in images and world coordinates.

Key operations:

  • Read WCS from FITS headers
  • Convert pixel coordinates to world coordinates (and vice versa)
  • Calculate image footprints
  • Access WCS parameters (reference pixel, projection, scale)
  • Create custom WCS objects

See: references/wcs_and_other_modules.md for WCS operations and transformations.

Additional Capabilities

The references/wcs_and_other_modules.md file also covers:

NDData and CCDData

Containers for n-dimensional datasets with metadata, uncertainty, masking, and WCS information.

Modeling

Framework for creating and fitting mathematical models to astronomical data.

Visualization

Tools for astronomical image display with appropriate stretching and scaling.

Constants

Physical and astronomical constants with proper units (speed of light, solar mass, Planck constant, etc.).

Convolution

Image processing kernels for smoothing and filtering.

Statistics

Robust statistical functions including sigma clipping and outlier rejection.

Installation

# Install astropy
uv pip install astropy

# With optional dependencies for full functionality
uv pip install astropy[all]

Common Workflows

Converting Coordinates Between Systems

from astropy.coordinates import SkyCoord
import astropy.units as u

# Create coordinate
c = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs')

# Transform to galactic
c_gal = c.galactic
print(f"l={c_gal.l.deg}, b={c_gal.b.deg}")

# Transform to alt-az (requires time and location)
from astropy.time import Time
from astropy.coordinates import EarthLocation, AltAz

observing_time = Time('2023-06-15 23:00:00')
observing_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)
aa_frame = AltAz(obstime=observing_time, location=observing_location)
c_altaz = c.transform_to(aa_frame)
print(f"Alt={c_altaz.alt.deg}, Az={c_altaz.az.deg}")

Reading and Analyzing FITS Files

from astropy.io import fits
import numpy as np

# Open FITS file
with fits.open('observation.fits') as hdul:
    # Display structure
    hdul.info()

    # Get image data and header
    data = hdul[1].data
    header = hdul[1].header

    # Access header values
    exptime = header['EXPTIME']
    filter_name = header['FILTER']

    # Analyze data
    mean = np.mean(data)
    median = np.median(data)
    print(f"Mean: {mean}, Median: {median}")

Cosmological Distance Calculations

from astropy.cosmology import Planck18
import astropy.units as u
import numpy as np

# Calculate distances at z=1.5
z = 1.5
d_L = Planck18.luminosity_distance(z)
d_A = Planck18.angular_diameter_distance(z)

print(f"Luminosity distance: {d_L}")
print(f"Angular diameter distance: {d_A}")

# Age of universe at that redshift
age = Planck18.age(z)
print(f"Age at z={z}: {age.to(u.Gyr)}")

# Lookback time
t_lookback = Planck18.lookback_time(z)
print(f"Lookback time: {t_lookback.to(u.Gyr)}")

Cross-Matching Catalogs

from astropy.table import Table
from astropy.coordinates import SkyCoord, match_coordinates_sky
import astropy.units as u

# Read catalogs
cat1 = Table.read('catalog1.fits')
cat2 = Table.read('catalog2.fits')

# Create coordinate objects
coords1 = SkyCoord(ra=cat1['RA']*u.degree, dec=cat1['DEC']*u.degree)
coords2 = SkyCoord(ra=cat2['RA']*u.degree, dec=cat2['DEC']*u.degree)

# Find matches
idx, sep, _ = coords1.match_to_catalog_sky(coords2)

# Filter by separation threshold
max_sep = 1 * u.arcsec
matches = sep < max_sep

# Create matched catalogs
cat1_matched = cat1[matches]
cat2_matched = cat2[idx[matches]]
print(f"Found {len(cat1_matched)} matches")

Best Practices

  1. Always use units: Attach units to quantities to avoid errors and ensure dimensional consistency
  2. Use context managers for FITS files: Ensures proper file closing
  3. Prefer arrays over loops: Process multiple coordinates/times as arrays for better performance
  4. Check coordinate frames: Verify the frame before transformations
  5. Use appropriate cosmology: Choose the right cosmological model for your analysis
  6. Handle missing data: Use masked columns for tables with missing values
  7. Specify time scales: Be explicit about time scales (UTC, TT, TDB) for precise timing
  8. Use QTable for unit-aware tables: When table columns have units
  9. Check WCS validity: Verify WCS before using transformations
  10. Cache frequently used values: Expensive calculations (e.g., cosmological distances) can be cached

Documentation and Resources

Reference Files

For detailed information on specific modules:

  • references/units.md - Units, quantities, conversions, and equivalencies
  • references/coordinates.md - Coordinate systems, transformations, and catalog matching
  • references/cosmology.md - Cosmological models and calculations
  • references/fits.md - FITS file operations and manipulation
  • references/tables.md - Table creation, I/O, and operations
  • references/time.md - Time formats, scales, and calculations
  • references/wcs_and_other_modules.md - WCS, NDData, modeling, visualization, constants, and utilities

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3.53%
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Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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