Astropy workflow skill. Use this skill when the user needs Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Astropy workflow skill. Use this skill when the user needs Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/astropy from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Astropy
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Capabilities, Additional Capabilities, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
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)
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
bash # Install astropy uv pip install astropy # With optional dependencies for full functionality uv pip install astropy[all] ### Converting Coordinates Between Systems python from astropy.coordinates import SkyCoord import astropy.units as u # Create coordinate c = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs') # Transform to galactic cgal = c.galactic print(f"l={cgal.l.deg}, b={cgal.b.deg}") # Transform to alt-az (requires time and location) from astropy.time import Time from astropy.coordinates import EarthLocation, AltAz observingtime = Time('2023-06-15 23:00:00') observinglocation = EarthLocation(lat=40u.deg, lon=-120u.deg) aaframe = AltAz(obstime=observingtime, location=observinglocation) caltaz = c.transformto(aaframe) print(f"Alt={caltaz.alt.deg}, Az={caltaz.az.deg}") ### Reading and Analyzing FITS Files python 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'] filtername = header['FILTER'] # Analyze data mean = np.mean(data) median = np.median(data) print(f"Mean: {mean}, Median: {median}") ### Cosmological Distance Calculations python from astropy.cosmology import Planck18 import astropy.units as u import numpy as np # Calculate distances at z=1.5 z = 1.5 dL = Planck18.luminositydistance(z) dA = Planck18.angulardiameterdistance(z) print(f"Luminosity distance: {dL}") print(f"Angular diameter distance: {dA}") # Age of universe at that redshift age = Planck18.age(z) print(f"Age at z={z}: {age.to(u.Gyr)}") # Lookback time tlookback = Planck18.lookbacktime(z) print(f"Lookback time: {tlookback.to(u.Gyr)}") ### Cross-Matching Catalogs python from astropy.table import Table from astropy.coordinates import SkyCoord, matchcoordinatessky 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.matchtocatalogsky(coords2) # Filter by separation threshold maxsep = 1 * u.arcsec matches = sep < maxsep # Create matched catalogs cat1matched = cat1[matches] cat2matched = cat2[idx[matches]] print(f"Found {len(cat1_matched)} matches")
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Imported Workflow Notes
Imported: Installation
# Install astropy
uv pip install astropy
# With optional dependencies for full functionality
uv pip install astropy[all]
Imported: 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 filewith 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)}")
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.
Imported: 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.
See:references/wcs_and_other_modules.md for WCS operations and transformations.
Examples
Example 1: Ask for the upstream workflow directly
Use @astropy to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @astropy against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @astropy for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @astropy using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: 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)
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Always use units: Attach units to quantities to avoid errors and ensure dimensional consistency
Use context managers for FITS files: Ensures proper file closing
Prefer arrays over loops: Process multiple coordinates/times as arrays for better performance
Check coordinate frames: Verify the frame before transformations
Use appropriate cosmology: Choose the right cosmological model for your analysis
Handle missing data: Use masked columns for tables with missing values
Specify time scales: Be explicit about time scales (UTC, TT, TDB) for precise timing
Imported Operating Notes
Imported: Best Practices
Always use units: Attach units to quantities to avoid errors and ensure dimensional consistency
Use context managers for FITS files: Ensures proper file closing
Prefer arrays over loops: Process multiple coordinates/times as arrays for better performance
Check coordinate frames: Verify the frame before transformations
Use appropriate cosmology: Choose the right cosmological model for your analysis
Handle missing data: Use masked columns for tables with missing values
Specify time scales: Be explicit about time scales (UTC, TT, TDB) for precise timing
Use QTable for unit-aware tables: When table columns have units
Check WCS validity: Verify WCS before using transformations
Cache frequently used values: Expensive calculations (e.g., cosmological distances) can be cached
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/astropy, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
