cosmosis-parameter-estimator
CosmoSIS cosmological parameter estimation skill for MCMC sampling and likelihood analysis
Best use case
cosmosis-parameter-estimator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
CosmoSIS cosmological parameter estimation skill for MCMC sampling and likelihood analysis
Teams using cosmosis-parameter-estimator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cosmosis-parameter-estimator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cosmosis-parameter-estimator Compares
| Feature / Agent | cosmosis-parameter-estimator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
CosmoSIS cosmological parameter estimation skill for MCMC sampling and likelihood analysis
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# CosmoSIS Parameter Estimator ## 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 1. **Pipeline Setup**: Configure modular analysis pipeline 2. **Likelihoods**: Build likelihood functions from data 3. **Priors**: Specify parameter priors 4. **Sampling**: Run MCMC with appropriate sampler 5. **Analysis**: Analyze chains and compute posteriors ## Tools/Libraries - CosmoSIS - emcee - GetDist
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