Execute a bounded soft-mode Score SDE recovery experiment using generated skills and mechanism checks.
Scanned 9/9/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill score_sde_recovery_harness --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: score_sde_recovery_harness
description: Execute a bounded soft-mode Score SDE recovery experiment using generated skills and mechanism checks.
---
# Score SDE Recovery Harness
Use this skill after module skills and runtime handoff exist. It assembles the generated SDE, loss, sampler, and probability-flow skills into an executable reduced experiment without reading the original repository.
## Inputs
- Attempt directory.
- Generated skills root.
- Runtime handoff path.
- Module plan target.
## Outputs
- `recovery/recovery_result.json`.
- `recovery/logs/training_trace.json`.
- `recovery/logs/generated_data_item.json`.
- `recovery/logs/generated_skill_invocations.json`.
- `recovery/source_manifest.json`.
## Workflow
1. Read the module plan and runtime handoff.
2. Construct a deterministic tiny synthetic batch.
3. Import or call every generated core skill.
4. Run one denoising score matching optimizer update.
5. Run PC sampler and probability-flow diagnostics.
6. Write all recovery artifacts and mechanism checks.
7. Run the Distiller recovery experiment validator.
## Validation
Run:
```bash
python tests/test_recovery_harness.py
python scripts/run_recovery.py --attempt-dir <attempt_dir> --skills-root <generated_skills_root>
```
## Limitations
This is a declared soft-mode proxy. It must not be reported as full CIFAR-10 FID, Inception Score, or bits/dim reproduction.
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