CosmoSIS Parameter Estimator

SkillDev tools

CosmoSIS cosmological parameter estimation skill for MCMC sampling and likelihood analysis

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the CosmoSIS Parameter Estimator skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/physics/skills/cosmosis-parameter-estimator/SKILL.md and read by ahel’s review.

Purpose

Provides expert guidance on CosmoSIS for cosmological parameter estimation, including modular likelihood construction and MCMC sampling.

Capabilities

Modular likelihood construction
Multiple sampler support (emcee, multinest, polychord)
Prior specification
Chain analysis and diagnostics
Plotting and visualization
Pipeline construction

Usage Guidelines

Pipeline Setup: Configure modular analysis pipeline
Likelihoods: Build likelihood functions from data
Priors: Specify parameter priors
Sampling: Run MCMC with appropriate sampler
Analysis: Analyze chains and compute posteriors

Tools/Libraries

CosmoSIS
emcee
GetDist

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: cosmosis-parameter-estimator
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter