emcee-mcmc-sampler
emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications
npx skills add a5c-ai/babysitter --skill emcee-mcmc-sampler --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.
# emcee MCMC Sampler ## Purpose Provides expert guidance on emcee for Bayesian parameter estimation in physics, including ensemble sampling and convergence diagnostics. ## Capabilities - Affine-invariant ensemble sampling - Parallel tempering support - Autocorrelation analysis - Convergence diagnostics - Prior/likelihood specification - Chain visualization ## Usage Guidelines 1. **Model Setup**: Define log-probability function 2. **Initialization**: Initialize walkers appropriately 3. **Sampling**: Run ensemble sampler 4. **Convergence**: Check autocorrelation and convergence 5. **Analysis**: Extract posterior distributions ## Tools/Libraries - emcee - corner - arviz
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the emcee-mcmc-sampler skill do?
emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill emcee-mcmc-sampler --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.