---
name: yogsoth-ai/reproducibility-protocol
source: https://app.decimal.ai/s/yogsoth-ai-reproducibility-protocol@1/SKILL.md
source_sha256: c2b99b6219fa
---

# Tactic: Reproducibility Protocol

## Orchestration Pattern

1. **Assess Reproducibility Requirements** → Determine level needed (exact, statistical, conceptual)
2. **seed-protocol-design** → Design random seed strategy for all stochastic components
3. **environment-specification** → Lock all software, hardware, and configuration dependencies
4. **Define Verification Plan** → How to confirm reproducibility (re-run subset, cross-machine test)
5. **Document Non-Determinism** → Identify and document unavoidable sources of variance

## Decision Criteria

| Reproducibility Level | Requirement | When to Use |
|----------------------|-------------|-------------|
| Exact (bit-for-bit) | Same hardware + deterministic ops | Debugging, verification |
| Statistical (within CI) | Same distribution of results | Standard research |
| Conceptual (same conclusion) | Same qualitative findings | Cross-platform validation |

## Quality Checks

- Are all random seeds documented and controllable?
- Is the full software environment captured (versions, dependencies)?
- Are hardware-specific non-determinisms identified (GPU atomics, cuDNN)?
- Is there a verification protocol (re-run N times, check variance)?
- Are data preprocessing steps deterministic or seeded?
- Is the order of data loading controlled?
- Are model initialization seeds separate from data shuffling seeds?
- Is there a plan for cross-machine reproducibility testing?

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## Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use |
| --- | --- |
| environment-specification | SOP: define complete experiment environment specification |
| seed-protocol-design | SOP: design random seed strategy for reproducibility |

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