Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill astronomy-cosmologygit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/astronomy-cosmology)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/astronomy-cosmology"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/astronomy-cosmology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/astronomy-cosmology"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/astronomy-cosmology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.00827 |
| Opus 5 | $0.00029 | $0.00413 |
| Sonnet 5 | $0.00012 | $0.00165 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
astronomy-cosmology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 54 lines · 58 tokens per session scan A 47de090a5a62
astronomy-cosmology is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 827 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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