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Skill

/design-randomness-protocol

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

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-randomness-protocol --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/design-randomness-protocol

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

design-randomness-protocol.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

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

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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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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