exoplanet-workflows
General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues.
npx skills add majiayu000/claude-skill-registry --skill exoplanet-workflows-benchflow-ai-skillsbench-2 --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.
# Exoplanet Detection Workflows This skill provides general guidance on exoplanet detection workflows, helping you choose the right approach for your data and goals. ## Overview Exoplanet detection from light curves typically involves: 1. Data loading and quality control 2. Preprocessing to remove instrumental and stellar noise 3. Period search using appropriate algorithms 4. Signal validation and characterization 5. Parameter estimation ## Pipeline Design Principles ### Key Stages 1. **Data Loading**: Understand your data format, columns, time system 2. **Quality Control**: Filter bad data points using quality flags 3. **Preprocessing**: Remove noise while preserving planetary signals 4. **Period Search**: Choose appropriate algorithm for signal type 5. **Validation**: Verify candidate is real, not artifact 6. **Refinement**: Improve period precision if candidate is strong ### Critical Decisions **What to preprocess?** - Remove outliers? Yes, but not too aggressively - Remove trends? Yes, stellar rotation masks transits - How much? Balance noise removal vs. signal preservation **Which period search algorithm?** - **TLS**: Best for transit-shaped signals (box-like dips) - **Lomb-Sc
- Overview
- Pipeline Design Principles
- Key Stages
- Critical Decisions
- Choosing the Right Method
- Transit Least Squares (TLS)
- Lomb-Scargle Periodogram
- Box Least Squares (BLS)
- Signal Validation
- Strong Candidate (TLS)
- Warning Signs
- How to Validate
- Multi-Planet Systems
- Common Issues and Solutions
pip install lightkurve transitleastsquares numpy matplotlib scipy
What does the exoplanet-workflows skill do?
General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill exoplanet-workflows-benchflow-ai-skillsbench-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.