camb-cosmology-calculator
CAMB cosmological perturbation skill for CMB power spectra, matter power spectra, and cosmological parameter estimation
npx skills add a5c-ai/babysitter --skill camb-cosmology-calculator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CAMB Cosmology Calculator ## Purpose Provides expert guidance on CAMB cosmological calculations, including CMB power spectra, matter power spectra, and parameter estimation. ## Capabilities - CMB temperature and polarization spectra - Matter power spectrum computation - Transfer function calculation - Dark energy equation of state models - Neutrino mass effects - Python/Fortran interface ## Usage Guidelines 1. **Parameter Setup**: Define cosmological parameters 2. **CMB Spectra**: Calculate temperature and polarization power spectra 3. **Matter Power**: Compute matter power spectrum and transfer functions 4. **Dark Energy**: Model dark energy with different equations of state 5. **Extensions**: Include neutrino masses and other physics ## Tools/Libraries - CAMB - CLASS - CosmoMC
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the camb-cosmology-calculator skill do?
CAMB cosmological perturbation skill for CMB power spectra, matter power spectra, and cosmological parameter estimation
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill camb-cosmology-calculator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.