Run bounded predictor-corrector reverse sampling diagnostics for score-based SDE mechanisms.
Scanned 9/9/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill score_sde_pc_sampler --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: score_sde_pc_sampler
description: Run bounded predictor-corrector reverse sampling diagnostics for score-based SDE mechanisms.
---
# Score SDE Predictor-Corrector Sampler
Use this skill to validate reverse-time SDE sampling behavior in a small recovery experiment. It should be invoked after an SDE and score function are defined.
## Inputs
- Initial scalar or vector state.
- Score function callable or oracle Gaussian score parameter.
- Number of reverse-time steps.
- Predictor step size and corrector settings.
## Outputs
- Final state and trajectory.
- Predictor and corrector invocation counts.
- Finite-value and movement diagnostics.
## Workflow
1. Initialize from a prior or deterministic proxy state.
2. Step from high time to epsilon.
3. Apply an Euler-style reverse predictor.
4. Apply Langevin-style corrector updates using the score.
5. Record every update count and final-state diagnostics.
## Validation
Run:
```bash
python tests/test_pc_sampler.py
python scripts/pc_sampler.py --initial 2.0 --steps 5 --corrector-steps 1
```
## Limitations
This skill validates sampler mechanics on tiny numeric states. It does not compute image FID or replace full pretrained sampling.
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