Astronomy Cosmology

Community

beita6969

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.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`astronomy-cosmology`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Astronomy Cosmology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/astronomy-cosmology
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/astronomy-cosmology .claude/skills/astronomy-cosmology

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Astronomy Cosmology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Astronomy Cosmology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Astronomy Cosmology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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).
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.
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.
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.
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.
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).
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

Frequently asked questions

What does the Astronomy Cosmology AI skill do?

Why use Astronomy Cosmology on TypingMind?

Because you install it once and use it with any model. Astronomy Cosmology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Astronomy Cosmology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/astronomy-cosmology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Astronomy Cosmology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Astronomy Cosmology?

As many as you like. As long as a model supports skills, you can use Astronomy Cosmology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Astronomy Cosmology AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

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