wind-tunnel-correlation
Specialized skill for correlating CFD predictions with experimental wind tunnel data
Profile →npx skills add a5c-ai/babysitter --skill wind-tunnel-correlation --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.
# Wind Tunnel Data Correlation Skill ## Purpose Enable accurate correlation between CFD predictions and experimental wind tunnel data through systematic data processing, correction methods, and statistical analysis. ## Capabilities - Data normalization and scaling procedures - Reynolds number and Mach number corrections - Wall interference and blockage corrections - Uncertainty quantification methods - Model calibration techniques - Statistical analysis and regression - Data quality assessment - Correlation report generation ## Usage Guidelines - Apply appropriate wind tunnel corrections based on test section geometry - Account for support interference effects in force measurements - Use proper Reynolds number scaling when comparing to flight conditions - Document uncertainty sources and propagation methods - Validate correlation quality using statistical metrics - Generate comprehensive correlation reports for design reviews ## Dependencies - MATLAB - Python scipy/numpy - Test data formats (DAT, CSV, HDF5) ## Process Integration - AE-002: Wind Tunnel Test Correlation - AE-003: Aerodynamic Database Generation
- Purpose
- Capabilities
- Usage Guidelines
- Dependencies
- Process Integration
What does the wind-tunnel-correlation skill do?
Specialized skill for correlating CFD predictions with experimental wind tunnel data
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill wind-tunnel-correlation --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.