Astropy Repo Skill
SkillDev tools"Use Astropy core for astronomy units, coordinates, times, tables,
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Astropy Repo Skill skill
What this skill tells your AI
The instructions your AI receives, as published by vectorspacelab/arex-skill in skills/repositories/repo-skills/astropy/SKILL.md and read by ahel’s review.
Use this skill when a task needs the Astropy core Python package for astronomy or astrophysics analysis. Astropy is a CPU scientific-Python library; selected workflows do not require GPU or accelerator backends. Use an installed Astropy package and the bundled references/scripts here rather than reopening the source repository.
Fast Start
import astropy
from astropy import units as u
print(astropy.__version__)
print((42 * u.km / u.s).to(u.m / u.s))
If plotting, fitting, convolution FFT paths, or common dataframe bridges are needed, prefer installing the common optional set:
python -m pip install "astropy[recommended]"
Use astropy[all] only for broad optional integrations such as HDF5, Parquet,
S3/fsspec, Jupyter, or additional astronomy packages.
Route by Task
- For units, quantities, equivalencies, custom units, and physical constants, read sub-skills/units-constants/SKILL.md.
- For
Time, timescales,SkyCoord, coordinate frames, transformations, separations, matching, and observation geometry, read sub-skills/time-coordinates/SKILL.md. - For
Table/QTable, unified I/O, FITS, ASCII/ECSV, VOTable, HDF5/Parquet notes, pandas/dataframe bridges, and table/FITS CLIs, read sub-skills/tables-io/SKILL.md. - For FITS WCS, pixel/world conversion, WCS validation,
NDData,CCDData, masks, uncertainties, and image metadata containers, read sub-skills/wcs-nddata/SKILL.md. - For image normalization, intervals, stretches, WCSAxes, RGB output,
fits2bitmap, kernels, direct convolution, and FFT convolution, read sub-skills/visualization-convolution/SKILL.md. - For models, fitters, compound models, constraints, robust statistics, histograms, time series, periodograms, and uncertainty distributions, read sub-skills/modeling-stats-timeseries/SKILL.md.
- For cosmology realizations/classes, distances, ages, redshift inversion, cosmology units, and serialization, read sub-skills/cosmology/SKILL.md.
- For install/import checks, optional extras, config/cache, remote data/IERS, logging/warnings, SAMP, and the general CLI catalog, read sub-skills/cli-config-data/SKILL.md.
Shared References and Scripts
- Read references/package-overview.md for the module map, install extras, and general workflow pattern.
- Read references/troubleshooting.md for cross-cutting install/import, optional dependency, remote-data, units, and FITS/WCS safety guidance.
- Read references/repo-provenance.md to check the source commit and evidence baseline before refreshing this skill.
- Router import metadata lives in references/repo-routing-metadata.json.
- Run scripts/astropy_smoke.py to verify a small installed-package API surface across the main sub-skill routes.
- Run scripts/astropy_cli_smoke.py to verify public Astropy console commands with safe help and optional temporary fixtures.
Operating Rules
- Keep units attached until a non-Astropy API requires raw arrays; then use
.to_value(unit)and record the unit. - Be explicit about
format=,scale=,frame=,unit=,origin=, andformatnames for I/O to avoid silent guessing. - Use temporary files for smoke checks and never mutate user FITS/table files unless the user explicitly requested that exact operation.
- For offline or reproducible tasks, disable or control remote downloads before coordinate/time/IERS/name-resolution operations.
- Validate scientific transformations numerically: unit round-trips, pixel/world round-trips, table read/write round-trips, fit residuals, or cosmology unit checks.
- Route optional dependency errors to the nearest sub-skill first, then to the root troubleshooting reference for install-level decisions.
Do Not Use This Skill For
- General NumPy/Pandas data processing without Astropy APIs, astronomy formats, units, times, or coordinates.
- Maintaining the Astropy repository, release engineering, CI, or documentation build workflows.
- Running broad Astropy test suites or benchmarks as a substitute for a focused user workflow.
- SAMP services, remote data downloads, or file-mutating CLI operations without explicit user approval and bounded temporary-file safeguards.
Signals
- GitHub stars
- 266
- Forks
- 21
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packagesK6low
bundled executables the agent is told to runK1binfo
installs-packages (in references/package-overview.md)K1binfo
installs-packages (in references/troubleshooting.md)K1binfo
installs-packages (in sub-skills/cli-config-data/references/operations.md)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Catalog kind
- skill
- Gateway key
astropy-vectorspacelab- Source
- github.com/vectorspacelab/arex-skill