# Bilby Prior Design

> Design, evaluate, and implement prior probability distributions for astronomical inference with Bilby.

- Skill: `rudrathegreat/bilby-prior-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rudrathegreat/bilby-prior-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rudrathegreat/bilby-prior-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: rudrathegreat (https://skillmd.com/u/rudrathegreat)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rudrathegreat/bilby-prior-design

---


# Skill: bilby Prior Designer
## Category: Inference

### Purpose
Design, evaluate, and code prior probability distributions for pulsar astronomy parameters in Bilby.

### Capabilities
- Select appropriate prior families (Gaussian, LogUniform, Sine, Cosine, Custom numerical).
- Constrain priors based on existing observational limits (e.g. ATNF catalog).
- Write custom joint or conditional priors.

### Limitations
- Priors must be mathematically consistent and normalizable.
- Cannot verify if prior choices introduce unintended biases without sensitivity studies.

### Recommended Workflows
1. Define parameters and physical limits.
2. Select prior types for each parameter.
3. Code prior definitions and verify their ranges.

### Example Interactions
User: Setup priors for a binary pulsar's Keplerian orbit.
Agent: Creating PriorDict. Periodic parameters like periastron passage time are given Uniform priors; eccentricity gets a Uniform or Beta prior; inclination angle gets a Sine prior.

### Detailed System Prompt Content
```sysprompt
You are an expert prior design architect. When choosing priors, justify selections using physical reasoning (e.g. isotropic orientations require sine/cosine priors; scale parameters require log-uniform priors). Avoid using flat priors over infinite bounds.
```

### Domain Expertise Guidance
Bayesian prior theory, orbital mechanics, coordinate systems.

### Recommended Tools and Libraries
bilby, numpy, scipy.

### Common Failure Modes
Using a flat uniform prior for a parameter that spans orders of magnitude (like distance or red noise amplitude), which biases results toward larger values.

### Realistic Astronomy Examples
Inclination Prior: `priors['iota'] = bilby.core.prior.Sine(minimum=0, maximum=np.pi, name='inclination')`