design-randomness-protocol
SkillMediaDesigns a randomness protocol for experiments so your agent can plan reproducible runs with fixed seeds and rules.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the design-randomness-protocol skill
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
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
design-randomness-protocol- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine