Installs into .claude/skills of the current project.
Are you the author of Rectified Velocity Regression?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-rectified-velocity-regression)
---
name: rectified_velocity_regression
description: Fit and audit a small rectified-flow velocity model with least-squares regression and optimizer evidence.
---
# Rectified Velocity Regression
Use this skill when rectified-flow records must be converted into a trained velocity field or when recovery must prove that an optimizer step happened. Do not use it to construct interpolation records.
## Inputs
- Records with `xt`, `t`, and `target_velocity`.
- Affine parameters `w_x`, `w_t`, and `b` per dimension.
- A learning rate and optional step count.
## Outputs
- Loss before and after training.
- Parameter values before and after training.
- Optimizer-change evidence and gradients.
## Workflow
1. Evaluate `v(x,t) = w_x * x + w_t * t + b` per dimension.
2. Compute mean squared error against `target_velocity`.
3. Compute analytic gradients and update parameters with SGD.
4. Report loss reduction and parameter changes.
## Validation
Run `python tests/test_velocity.py` or validate with `--run-tests`.
## Limitations
This deterministic affine model is intended for bounded recovery and unit tests; it is not a replacement for the paper's full neural image model.
