reproducibility-protocol
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/reproducibility-protocol1. **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
SKILL.md
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--- name: reproducibility-protocol description: "Ensure experiment reproducibility through systematic environment and seed control" version: 1.0.0 category: experiment-execution type: tactic used-by: experiment-design orchestrates: - seed-protocol-design - environment-specification --- # 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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