Individual skill

astronomy-inference

Carries astronomical data from file to posterior with the conventions the field expects, covering astropy units, coordinates, FITS, WCS and Time with barycentric corrections, photometric systems and magnitude arithmetic, period finding with the astropy Lomb-Scargle periodogram and its false-alarm levels, Bayesian parameter estimation with emcee and nested sampling with dynesty, convergence diagnostics such as autocorrelation time and R-hat, uncertainty propagation through samples, and explicit reporting of priors. Use for light curves, photometry, spectroscopy, orbit or transit fits and any astronomical parameter estimation; use astropy for plain library usage and bayesian-inference for non-astronomical models.

backend/cli/skills/physics/astronomy-inference/SKILL.md

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# Astronomy inference

Astronomy overloads its conventions: magnitudes are logarithmic and zero-point dependent,
times have scales as well as formats, coordinates have frames and epochs, and a periodogram
peak is a candidate rather than a detection. Fix the conventions first, then infer, then
report the priors and diagnostics alongside the posterior.

## astropy foundations

1. Attach units at input and keep them until the final print: `astropy.units` Quantities
   with `.to(u.Jy)`, spectral conversions via `equivalencies=u.spectral()` and flux
   density conversions via `u.spectral_density(wavelength)`; constants from
   `astropy.constants`. Stripping units early is how a factor of 1e-23 disappears.
2. Coordinates: `SkyCoord(ra, dec, unit="deg", frame="icrs")`; ICRS, FK5 (J2000) and