astronomy-cosmology
Analyzes astronomical observations and cosmological models including telescope data processing, celestial mechanics calculations, stellar evolution, galaxy classification, and cosmological parameter estimation; trigger when users discuss stars, galaxies, exoplanets, dark matter, or the universe's large-scale structure.
Best use case
astronomy-cosmology is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyzes astronomical observations and cosmological models including telescope data processing, celestial mechanics calculations, stellar evolution, galaxy classification, and cosmological parameter estimation; trigger when users discuss stars, galaxies, exoplanets, dark matter, or the universe's large-scale structure.
Teams using astronomy-cosmology should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/astronomy-cosmology/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How astronomy-cosmology Compares
| Feature / Agent | astronomy-cosmology | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes astronomical observations and cosmological models including telescope data processing, celestial mechanics calculations, stellar evolution, galaxy classification, and cosmological parameter estimation; trigger when users discuss stars, galaxies, exoplanets, dark matter, or the universe's large-scale structure.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## When to Trigger Activate this skill when the user mentions: - Telescope observations, photometry, spectroscopy, astrometry - Celestial mechanics, orbital calculations, Kepler's laws - Stellar evolution, HR diagram, spectral classification - Galaxy morphology, redshift, distance ladder - Cosmological models, dark matter, dark energy, CMB - Exoplanet detection, transit method, radial velocity - Gravitational waves, black holes, neutron stars ## Step-by-Step Methodology 1. **Define the astronomical question** - Specify the object type (star, galaxy, nebula, exoplanet), observational band (optical, radio, X-ray, IR), and physical quantity of interest (distance, mass, luminosity, composition). 2. **Data acquisition** - Identify relevant surveys and archives: Gaia for astrometry, SDSS for optical spectra/photometry, 2MASS/WISE for IR, Chandra for X-ray. Download data using VO (Virtual Observatory) tools or API queries. 3. **Calibration and reduction** - Apply bias subtraction, flat-fielding, wavelength/flux calibration. For photometry: aperture or PSF fitting. For spectroscopy: sky subtraction, continuum normalization. Report signal-to-noise ratios. 4. **Physical parameter derivation** - Compute distances (parallax, standard candles, redshift-distance relation using appropriate cosmology). Derive masses (Kepler's third law, virial theorem, mass-luminosity relation). Determine compositions from spectral line analysis. 5. **Modeling** - Fit observational data with physical models: stellar atmosphere models (ATLAS, PHOENIX), N-body simulations for dynamics, cosmological models (LCDM, wCDM). Use MCMC or nested sampling for parameter estimation. 6. **Cosmological calculations** - Use standard cosmological parameters (H0, Omega_m, Omega_Lambda). Compute comoving distances, lookback times, luminosity distances. Note current tensions (H0 tension between early and late universe). 7. **Visualization** - Produce standard astronomical plots: HR diagrams, light curves, spectra, sky maps in appropriate coordinate systems (equatorial, galactic). Use logarithmic scales where appropriate. ## Key Databases and Tools - **NASA/IPAC Extragalactic Database (NED)** - Extragalactic object data - **SIMBAD / VizieR** - Stellar object data and catalog queries - **Gaia Archive** - Astrometric and photometric data - **SDSS SkyServer** - Optical survey data - **NASA Exoplanet Archive** - Confirmed exoplanet parameters - **Astropy** - Python astronomy library - **MAST (STScI)** - Hubble, JWST, and other mission archives ## Output Format - Coordinates in standard systems: RA/Dec (J2000) or Galactic (l, b). - Distances with method and uncertainty (parallax, photometric, spectroscopic). - Physical quantities in CGS or SI with astronomical conventions (solar units, parsecs, magnitudes). - Spectra with wavelength/frequency axis, flux units, and line identifications. ## Quality Checklist - [ ] Coordinate system and epoch explicitly stated - [ ] Distance method and its systematic uncertainties discussed - [ ] Cosmological parameters (H0, Omega_m) specified when used - [ ] Photometric system (Vega, AB) identified for magnitudes - [ ] Extinction/reddening corrections applied where relevant - [ ] Instrument and survey limitations acknowledged - [ ] Error propagation through derived quantities - [ ] Known systematic effects (selection bias, Malmquist bias) addressed
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