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Design Randomness Protocol

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Identify randomness sources and define seed/repetition/propagation rules sufficient for the intended reproducibility level.

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  • Added September 24, 2026
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Scanned September 24, 2026

npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-randomness-protocol --agent claude-code

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SKILL.md
---
name: design-randomness-protocol
description: "Identify randomness sources and define seed/repetition/propagation rules sufficient for the intended reproducibility level."
---

# design-randomness-protocol

## Purpose

Identify randomness sources and define seed/repetition/propagation rules sufficient for the intended reproducibility level.

## Input contract

```yaml
required: [randomness_sources, reproducibility_target, run_plan]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]
```

## Procedure

1. Validate the typed inputs and state the decision this operation must support.
2. Apply the declared operation to the named object; record intermediate values that affect interpretation.
3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

## Output contract

```yaml
produces: [design_randomness_protocol_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]
```

## Quality gates

- Inputs are named scientific objects with compatible schemas.
- Every material result has a derivation or source reference.
- Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.

## Failure and counterexamples

Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.

## Provenance map

- intermediate: experiment-execution/seed-protocol-design

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