[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFA9YQ51JG7AP9R7DM521W":3,"skill-same:01M6AFA9YQ51JG7AP9R7DM521W":113},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6AFA9YQ51JG7AP9R7DM521W","astropy",null,"Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33602,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"coordinate","when","for","tables","with","processing","unit","astrophysics","time","world","data analysis","files","use","analysis","data","file","library","tasks","python","2026-11-10 10:12:30",false,"---\nname: astropy\ndescription: Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.\n---\n\n# Astropy\n\n## Overview\n\nAstropy 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.\n\n## When to Use This Skill\n\nUse astropy when tasks involve:\n- Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)\n- Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)\n- Reading, writing, or manipulating FITS files (images or tables)\n- Cosmological calculations (luminosity distance, lookback time, Hubble parameter)\n- Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)\n- Table operations (reading catalogs, cross-matching, filtering, joining)\n- WCS transformations between pixel and world coordinates\n- Astronomical constants and calculations\n\n## Quick Start\n\n```python\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.time import Time\nfrom astropy.io import fits\nfrom astropy.table import Table\nfrom astropy.cosmology import Planck18\n\n# Units and quantities\ndistance = 100 * u.pc\ndistance_km = distance.to(u.km)\n\n# Coordinates\ncoord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')\ncoord_galactic = coord.galactic\n\n# Time\nt = Time('2023-01-15 12:30:00')\njd = t.jd  # Julian Date\n\n# FITS files\ndata = fits.getdata('image.fits')\nheader = fits.getheader('image.fits')\n\n# Tables\ntable = Table.read('catalog.fits')\n\n# Cosmology\nd_L = Planck18.luminosity_distance(z=1.0)\n```\n\n## Core Capabilities\n\n### 1. Units and Quantities (`astropy.units`)\n\nHandle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.\n\n**Key operations:**\n- Create quantities by multiplying values with units\n- Convert between units using `.to()` method\n- Perform arithmetic with automatic unit handling\n- Use equivalencies for domain-specific conversions (spectral, doppler, parallax)\n- Work with logarithmic units (magnitudes, decibels)\n\n**See:** `references\u002Funits.md` for comprehensive documentation, unit systems, equivalencies, performance optimization, and unit arithmetic.\n\n### 2. Coordinate Systems (`astropy.coordinates`)\n\nRepresent celestial positions and transform between different coordinate frames.\n\n**Key operations:**\n- Create coordinates with `SkyCoord` in any frame (ICRS, Galactic, FK5, AltAz, etc.)\n- Transform between coordinate systems\n- Calculate angular separations and position angles\n- Match coordinates to catalogs\n- Include distance for 3D coordinate operations\n- Handle proper motions and radial velocities\n- Query named objects from online databases\n\n**See:** `references\u002Fcoordinates.md` for detailed coordinate frame descriptions, transformations, observer-dependent frames (AltAz), catalog matching, and performance tips.\n\n### 3. Cosmological Calculations (`astropy.cosmology`)\n\nPerform cosmological calculations using standard cosmological models.\n\n**Key operations:**\n- Use built-in cosmologies (Planck18, WMAP9, etc.)\n- Create custom cosmological models\n- Calculate distances (luminosity, comoving, angular diameter)\n- Compute ages and lookback times\n- Determine Hubble parameter at any redshift\n- Calculate density parameters and volumes\n- Perform inverse calculations (find z for given distance)\n\n**See:** `references\u002Fcosmology.md` for available models, distance calculations, time calculations, density parameters, and neutrino effects.\n\n### 4. FITS File Handling (`astropy.io.fits`)\n\nRead, write, and manipulate FITS (Flexible Image Transport System) files.\n\n**Key operations:**\n- Open FITS files with context managers\n- Access HDUs (Header Data Units) by index or name\n- Read and modify headers (keywords, comments, history)\n- Work with image data (NumPy arrays)\n- Handle table data (binary and ASCII tables)\n- Create new FITS files (single or multi-extension)\n- Use memory mapping for large files\n- Access remote FITS files (S3, HTTP)\n\n**See:** `references\u002Ffits.md` for comprehensive file operations, header manipulation, image and table handling, multi-extension files, and performance considerations.\n\n### 5. Table Operations (`astropy.table`)\n\nWork with tabular data with support for units, metadata, and various file formats.\n\n**Key operations:**\n- Create tables from arrays, lists, or dictionaries\n- Read\u002Fwrite tables in multiple formats (FITS, CSV, HDF5, VOTable)\n- Access and modify columns and rows\n- Sort, filter, and index tables\n- Perform database-style operations (join, group, aggregate)\n- Stack and concatenate tables\n- Work with unit-aware columns (QTable)\n- Handle missing data with masking\n\n**See:** `references\u002Ftables.md` for table creation, I\u002FO operations, data manipulation, sorting, filtering, joins, grouping, and performance tips.\n\n### 6. Time Handling (`astropy.time`)\n\nPrecise time representation and conversion between time scales and formats.\n\n**Key operations:**\n- Create Time objects in various formats (ISO, JD, MJD, Unix, etc.)\n- Convert between time scales (UTC, TAI, TT, TDB, etc.)\n- Perform time arithmetic with TimeDelta\n- Calculate sidereal time for observers\n- Compute light travel time corrections (barycentric, heliocentric)\n- Work with time arrays efficiently\n- Handle masked (missing) times\n\n**See:** `references\u002Ftime.md` for time formats, time scales, conversions, arithmetic, observing features, and precision handling.\n\n### 7. World Coordinate System (`astropy.wcs`)\n\nTransform between pixel coordinates in images and world coordinates.\n\n**Key operations:**\n- Read WCS from FITS headers\n- Convert pixel coordinates to world coordinates (and vice versa)\n- Calculate image footprints\n- Access WCS parameters (reference pixel, projection, scale)\n- Create custom WCS objects\n\n**See:** `references\u002Fwcs_and_other_modules.md` for WCS operations and transformations.\n\n## Additional Capabilities\n\nThe `references\u002Fwcs_and_other_modules.md` file also covers:\n\n### NDData and CCDData\nContainers for n-dimensional datasets with metadata, uncertainty, masking, and WCS information.\n\n### Modeling\nFramework for creating and fitting mathematical models to astronomical data.\n\n### Visualization\nTools for astronomical image display with appropriate stretching and scaling.\n\n### Constants\nPhysical and astronomical constants with proper units (speed of light, solar mass, Planck constant, etc.).\n\n### Convolution\nImage processing kernels for smoothing and filtering.\n\n### Statistics\nRobust statistical functions including sigma clipping and outlier rejection.\n\n## Installation\n\n```bash\n# Install astropy\nuv pip install astropy\n\n# With optional dependencies for full functionality\nuv pip install astropy[all]\n```\n\n## Common Workflows\n\n### Converting Coordinates Between Systems\n\n```python\nfrom astropy.coordinates import SkyCoord\nimport astropy.units as u\n\n# Create coordinate\nc = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs')\n\n# Transform to galactic\nc_gal = c.galactic\nprint(f\"l={c_gal.l.deg}, b={c_gal.b.deg}\")\n\n# Transform to alt-az (requires time and location)\nfrom astropy.time import Time\nfrom astropy.coordinates import EarthLocation, AltAz\n\nobserving_time = Time('2023-06-15 23:00:00')\nobserving_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)\naa_frame = AltAz(obstime=observing_time, location=observing_location)\nc_altaz = c.transform_to(aa_frame)\nprint(f\"Alt={c_altaz.alt.deg}, Az={c_altaz.az.deg}\")\n```\n\n### Reading and Analyzing FITS Files\n\n```python\nfrom astropy.io import fits\nimport numpy as np\n\n# Open FITS file\nwith fits.open('observation.fits') as hdul:\n    # Display structure\n    hdul.info()\n\n    # Get image data and header\n    data = hdul[1].data\n    header = hdul[1].header\n\n    # Access header values\n    exptime = header['EXPTIME']\n    filter_name = header['FILTER']\n\n    # Analyze data\n    mean = np.mean(data)\n    median = np.median(data)\n    print(f\"Mean: {mean}, Median: {median}\")\n```\n\n### Cosmological Distance Calculations\n\n```python\nfrom astropy.cosmology import Planck18\nimport astropy.units as u\nimport numpy as np\n\n# Calculate distances at z=1.5\nz = 1.5\nd_L = Planck18.luminosity_distance(z)\nd_A = Planck18.angular_diameter_distance(z)\n\nprint(f\"Luminosity distance: {d_L}\")\nprint(f\"Angular diameter distance: {d_A}\")\n\n# Age of universe at that redshift\nage = Planck18.age(z)\nprint(f\"Age at z={z}: {age.to(u.Gyr)}\")\n\n# Lookback time\nt_lookback = Planck18.lookback_time(z)\nprint(f\"Lookback time: {t_lookback.to(u.Gyr)}\")\n```\n\n### Cross-Matching Catalogs\n\n```python\nfrom astropy.table import Table\nfrom astropy.coordinates import SkyCoord, match_coordinates_sky\nimport astropy.units as u\n\n# Read catalogs\ncat1 = Table.read('catalog1.fits')\ncat2 = Table.read('catalog2.fits')\n\n# Create coordinate objects\ncoords1 = SkyCoord(ra=cat1['RA']*u.degree, dec=cat1['DEC']*u.degree)\ncoords2 = SkyCoord(ra=cat2['RA']*u.degree, dec=cat2['DEC']*u.degree)\n\n# Find matches\nidx, sep, _ = coords1.match_to_catalog_sky(coords2)\n\n# Filter by separation threshold\nmax_sep = 1 * u.arcsec\nmatches = sep \u003C max_sep\n\n# Create matched catalogs\ncat1_matched = cat1[matches]\ncat2_matched = cat2[idx[matches]]\nprint(f\"Found {len(cat1_matched)} matches\")\n```\n\n## Best Practices\n\n1. **Always use units**: Attach units to quantities to avoid errors and ensure dimensional consistency\n2. **Use context managers for FITS files**: Ensures proper file closing\n3. **Prefer arrays over loops**: Process multiple coordinates\u002Ftimes as arrays for better performance\n4. **Check coordinate frames**: Verify the frame before transformations\n5. **Use appropriate cosmology**: Choose the right cosmological model for your analysis\n6. **Handle missing data**: Use masked columns for tables with missing values\n7. **Specify time scales**: Be explicit about time scales (UTC, TT, TDB) for precise timing\n8. **Use QTable for unit-aware tables**: When table columns have units\n9. **Check WCS validity**: Verify WCS before using transformations\n10. **Cache frequently used values**: Expensive calculations (e.g., cosmological distances) can be cached\n\n## Documentation and Resources\n\n- Official Astropy Documentation: https:\u002F\u002Fdocs.astropy.org\u002Fen\u002Fstable\u002F\n- Tutorials: https:\u002F\u002Flearn.astropy.org\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Fastropy\u002Fastropy\n\n## Reference Files\n\nFor detailed information on specific modules:\n- `references\u002Funits.md` - Units, quantities, conversions, and equivalencies\n- `references\u002Fcoordinates.md` - Coordinate systems, transformations, and catalog matching\n- `references\u002Fcosmology.md` - Cosmological models and calculations\n- `references\u002Ffits.md` - FITS file operations and manipulation\n- `references\u002Ftables.md` - Table creation, I\u002FO, and operations\n- `references\u002Ftime.md` - Time formats, scales, and calculations\n- `references\u002Fwcs_and_other_modules.md` - WCS, NDData, modeling, visualization, constants, and utilities\n","# Astropy\n\n## Overview\n\nAstropy 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.\n\n## When to Use This Skill\n\nUse astropy when tasks involve:\n- Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)\n- Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)\n- Reading, writing, or manipulating FITS files (images or tables)\n- Cosmological calculations (luminosity distance, lookback time, Hubble parameter)\n- Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)\n- Table operations (reading catalogs, cross-matching, filtering, joining)\n- WCS transformations between pixel and world coordinates\n- Astronomical constants and calculations\n\n## Quick Start\n\n```python\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.time import Time\nfrom astropy.io import fits\nfrom astropy.table import Table\nfrom astropy.cosmology import Planck18\n\n# Units and quantities\ndistance = 100 * u.pc\ndistance_km = distance.to(u.km)\n\n# Coordinates\ncoord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')\ncoord_galactic = coord.galactic\n\n# Time\nt = Time('2023-01-15 12:30:00')\njd = t.jd  # Julian Date\n\n# FITS files\ndata = fits.getdata('image.fits')\nheader = fits.getheader('image.fits')\n\n# Tables\ntable = Table.read('catalog.fits')\n\n# Cosmology\nd_L = Planck18.luminosity_distance(z=1.0)\n```\n\n## Core Capabilities\n\n### 1. Units and Quantities (`astropy.units`)\n\nHandle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.\n\n**Key operations:**\n- Create quantities by multiplying values with units\n- Convert between units using `.to()` method\n- Perform arithmetic with automatic unit handling\n- Use equivalencies for domain-specific conversions (spectral, doppler, parallax)\n- Work with logarithmic units (magnitudes, decibels)\n\n**See:** `references\u002Funits.md` for comprehensive documentation, unit systems, equivalencies, performance optimization, and unit arithmetic.\n\n### 2. Coordinate Systems (`astropy.coordinates`)\n\nRepresent celestial positions and transform between different coordinate frames.\n\n**Key operations:**\n- Create coordinates with `SkyCoord` in any frame (ICRS, Galactic, FK5, AltAz, etc.)\n- Transform between coordinate systems\n- Calculate angular separations and position angles\n- Match coordinates to catalogs\n- Include distance for 3D coordinate operations\n- Handle proper motions and radial velocities\n- Query named objects from online databases\n\n**See:** `references\u002Fcoordinates.md` for detailed coordinate frame descriptions, transformations, observer-dependent frames (AltAz), catalog matching, and performance tips.\n\n### 3. Cosmological Calculations (`astropy.cosmology`)\n\nPerform cosmological calculations using standard cosmological models.\n\n**Key operations:**\n- Use built-in cosmologies (Planck18, WMAP9, etc.)\n- Create custom cosmological models\n- Calculate distances (luminosity, comoving, angular diameter)\n- Compute ages and lookback times\n- Determine Hubble parameter at any redshift\n- Calculate density parameters and volumes\n- Perform inverse calculations (find z for given distance)\n\n**See:** `references\u002Fcosmology.md` for available models, distance calculations, time calculations, density parameters, and neutrino effects.\n\n### 4. FITS File Handling (`astropy.io.fits`)\n\nRead, write, and manipulate FITS (Flexible Image Transport System) files.\n\n**Key operations:**\n- Open FITS files with context managers\n- Access HDUs (Header Data Units) by index or name\n- Read and modify headers (keywords, comments, history)\n- Work with image data (NumPy arrays)\n- Handle table data (binary and ASCII tables)\n- Create new FITS files (single or multi-extension)\n- Use memory mapping for large files\n- Access remote FITS files (S3, HTTP)\n\n**See:** `references\u002Ffits.md` for comprehensive file operations, header manipulation, image and table handling, multi-extension files, and performance considerations.\n\n### 5. Table Operations (`astropy.table`)\n\nWork with tabular data with support for units, metadata, and various file formats.\n\n**Key operations:**\n- Create tables from arrays, lists, or dictionaries\n- Read\u002Fwrite tables in multiple formats (FITS, CSV, HDF5, VOTable)\n- Access and modify columns and rows\n- Sort, filter, and index tables\n- Perform database-style operations (join, group, aggregate)\n- Stack and concatenate tables\n- Work with unit-aware columns (QTable)\n- Handle missing data with masking\n\n**See:** `references\u002Ftables.md` for table creation, I\u002FO operations, data manipulation, sorting, filtering, joins, grouping, and performance tips.\n\n### 6. Time Handling (`astropy.time`)\n\nPrecise time representation and conversion between time scales and formats.\n\n**Key operations:**\n- Create Time objects in various formats (ISO, JD, MJD, Unix, etc.)\n- Convert between time scales (UTC, TAI, TT, TDB, etc.)\n- Perform time arithmetic with TimeDelta\n- Calculate sidereal time for observers\n- Compute light travel time corrections (barycentric, heliocentric)\n- Work with time arrays efficiently\n- Handle masked (missing) times\n\n**See:** `references\u002Ftime.md` for time formats, time scales, conversions, arithmetic, observing features, and precision handling.\n\n### 7. World Coordinate System (`astropy.wcs`)\n\nTransform between pixel coordinates in images and world coordinates.\n\n**Key operations:**\n- Read WCS from FITS headers\n- Convert pixel coordinates to world coordinates (and vice versa)\n- Calculate image footprints\n- Access WCS parameters (reference pixel, projection, scale)\n- Create custom WCS objects\n\n**See:** `references\u002Fwcs_and_other_modules.md` for WCS operations and transformations.\n\n## Additional Capabilities\n\nThe `references\u002Fwcs_and_other_modules.md` file also covers:\n\n### NDData and CCDData\nContainers for n-dimensional datasets with metadata, uncertainty, masking, and WCS information.\n\n### Modeling\nFramework for creating and fitting mathematical models to astronomical data.\n\n### Visualization\nTools for astronomical image display with appropriate stretching and scaling.\n\n### Constants\nPhysical and astronomical constants with proper units (speed of light, solar mass, Planck constant, etc.).\n\n### Convolution\nImage processing kernels for smoothing and filtering.\n\n### Statistics\nRobust statistical functions including sigma clipping and outlier rejection.\n\n## Installation\n\n```bash\n# Install astropy\nuv pip install astropy\n\n# With optional dependencies for full functionality\nuv pip install astropy[all]\n```\n\n## Common Workflows\n\n### Converting Coordinates Between Systems\n\n```python\nfrom astropy.coordinates import SkyCoord\nimport astropy.units as u\n\n# Create coordinate\nc = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs')\n\n# Transform to galactic\nc_gal = c.galactic\nprint(f\"l={c_gal.l.deg}, b={c_gal.b.deg}\")\n\n# Transform to alt-az (requires time and location)\nfrom astropy.time import Time\nfrom astropy.coordinates import EarthLocation, AltAz\n\nobserving_time = Time('2023-06-15 23:00:00')\nobserving_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)\naa_frame = AltAz(obstime=observing_time, location=observing_location)\nc_altaz = c.transform_to(aa_frame)\nprint(f\"Alt={c_altaz.alt.deg}, Az={c_altaz.az.deg}\")\n```\n\n### Reading and Analyzing FITS Files\n\n```python\nfrom astropy.io import fits\nimport numpy as np\n\n# Open FITS file\nwith fits.open('observation.fits') as hdul:\n    # Display structure\n    hdul.info()\n\n    # Get image data and header\n    data = hdul[1].data\n    header = hdul[1].header\n\n    # Access header values\n    exptime = header['EXPTIME']\n    filter_name = header['FILTER']\n\n    # Analyze data\n    mean = np.mean(data)\n    median = np.median(data)\n    print(f\"Mean: {mean}, Median: {median}\")\n```\n\n### Cosmological Distance Calculations\n\n```python\nfrom astropy.cosmology import Planck18\nimport astropy.units as u\nimport numpy as np\n\n# Calculate distances at z=1.5\nz = 1.5\nd_L = Planck18.luminosity_distance(z)\nd_A = Planck18.angular_diameter_distance(z)\n\nprint(f\"Luminosity distance: {d_L}\")\nprint(f\"Angular diameter distance: {d_A}\")\n\n# Age of universe at that redshift\nage = Planck18.age(z)\nprint(f\"Age at z={z}: {age.to(u.Gyr)}\")\n\n# Lookback time\nt_lookback = Planck18.lookback_time(z)\nprint(f\"Lookback time: {t_lookback.to(u.Gyr)}\")\n```\n\n### Cross-Matching Catalogs\n\n```python\nfrom astropy.table import Table\nfrom astropy.coordinates import SkyCoord, match_coordinates_sky\nimport astropy.units as u\n\n# Read catalogs\ncat1 = Table.read('catalog1.fits')\ncat2 = Table.read('catalog2.fits')\n\n# Create coordinate objects\ncoords1 = SkyCoord(ra=cat1['RA']*u.degree, dec=cat1['DEC']*u.degree)\ncoords2 = SkyCoord(ra=cat2['RA']*u.degree, dec=cat2['DEC']*u.degree)\n\n# Find matches\nidx, sep, _ = coords1.match_to_catalog_sky(coords2)\n\n# Filter by separation threshold\nmax_sep = 1 * u.arcsec\nmatches = sep \u003C max_sep\n\n# Create matched catalogs\ncat1_matched = cat1[matches]\ncat2_matched = cat2[idx[matches]]\nprint(f\"Found {len(cat1_matched)} matches\")\n```\n\n## Best Practices\n\n1. **Always use units**: Attach units to quantities to avoid errors and ensure dimensional consistency\n2. **Use context managers for FITS files**: Ensures proper file closing\n3. **Prefer arrays over loops**: Process multiple coordinates\u002Ftimes as arrays for better performance\n4. **Check coordinate frames**: Verify the frame before transformations\n5. **Use appropriate cosmology**: Choose the right cosmological model for your analysis\n6. **Handle missing data**: Use masked columns for tables with missing values\n7. **Specify time scales**: Be explicit about time scales (UTC, TT, TDB) for precise timing\n8. **Use QTable for unit-aware tables**: When table columns have units\n9. **Check WCS validity**: Verify WCS before using transformations\n10. **Cache frequently used values**: Expensive calculations (e.g., cosmological distances) can be cached\n\n## Documentation and Resources\n\n- Official Astropy Documentation: https:\u002F\u002Fdocs.astropy.org\u002Fen\u002Fstable\u002F\n- Tutorials: https:\u002F\u002Flearn.astropy.org\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Fastropy\u002Fastropy\n\n## Reference Files\n\nFor detailed information on specific modules:\n- `references\u002Funits.md` - Units, quantities, conversions, and equivalencies\n- `references\u002Fcoordinates.md` - Coordinate systems, transformations, and catalog matching\n- `references\u002Fcosmology.md` - Cosmological models and calculations\n- `references\u002Ffits.md` - FITS file operations and manipulation\n- `references\u002Ftables.md` - Table creation, I\u002FO, and operations\n- `references\u002Ftime.md` - Time formats, scales, and calculations\n- `references\u002Fwcs_and_other_modules.md` - WCS, NDData, modeling, visualization, constants, and utilities\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy",3963,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-15 01:32:48",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add davila7\u002Fclaude-code-templates --skill astropy",{"tool":67,"command":68},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill astropy",{"tool":70,"command":71},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill astropy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002FSKILL.md",true,[75,81,85,89,93,97,101,105,109],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"mocking.md","REFERENCE","参考文档",1481,"https:\u002F\u002Fgithub.com\u002Fcolliery-io\u002Fkairos\u002Fblob\u002Fmain\u002Fplugin\u002Fskills\u002Fengineering\u002Ftdd\u002Fmocking.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fcoordinates.md",7111,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Fcoordinates.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fcosmology.md",6482,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Fcosmology.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Ffits.md",8670,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Ffits.md",{"path":94,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Ftables.md",9385,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Ftables.md",{"path":98,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Ftime.md",9083,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Ftime.md",{"path":102,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Funits.md",3766,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Funits.md",{"path":106,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fwcs_and_other_modules.md",8515,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fastropy\u002Freferences\u002Fwcs_and_other_modules.md",{"path":110,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":111,"sourceHtmlUrl":112},"tests.md",2214,"https:\u002F\u002Fgithub.com\u002Fcolliery-io\u002Fkairos\u002Fblob\u002Fmain\u002Fplugin\u002Fskills\u002Fengineering\u002Ftdd\u002Ftests.md",[114,138,165,198,232,256],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":46},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[120],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[122,123,124,125,126,29,127,128,39,28,129,31,130,131,132,133,134,135,136,137],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":145,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[144],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[146,147,148,149,150,151,152,153,154,155,156,28,157,158,159,160,161,162,163,164],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":180,"publishedAt":46},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[171,174,175],{"categoryNo":172,"categoryName":173,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":176,"categoryName":177,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[181,182,183,184,185,186,187,29,188,189,190,191,192,193,194,39,195,196,28,197],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":209,"publishedAt":46},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[206],{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[210,211,212,213,214,215,216,217,218,219,220,221,222,42,223,224,225,226,227,228,229,230,29,231],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":236,"sourceHtmlUrl":237,"categories":238,"keywords":243,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[239,242],{"categoryNo":240,"categoryName":241,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},[244,245,246,247,248,211,249,220,221,250,251,252,253,29,254,31,255],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":257,"name":258,"nameZh":6,"description":259,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":260,"sourceHtmlUrl":261,"categories":262,"keywords":270,"publishedAt":46},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[263,266,267],{"categoryNo":264,"categoryName":265,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":268,"categoryName":269,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000036","内容创作者",[271,272,273,274,275,276,41,277,213,278,42,157,279,280,281,282,283,193,284,28,285,40,286,287,288,135,289],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]