design-randomness-protocol
SkillMediaDesigns a randomness protocol for experiments so your agent can plan reproducible runs with fixed seeds and rules.
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Details
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About this skill
Identify randomness sources and define seed/repetition/propagation rules sufficient for the intended reproducibility level.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/design-randomness-protocol/SKILL.md and read by Ahel’s review.
Purpose
Identify randomness sources and define seed/repetition/propagation rules sufficient for the intended reproducibility level.
Input contract
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
- Validate the typed inputs and state the decision this operation must support.
- Apply the declared operation to the named object; record intermediate values that affect interpretation.
- Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.
Output contract
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
Signals
- GitHub stars
- 503
- Forks
- 42
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
design-randomness-protocol- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine