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/evaluate-scenario-robustness

Aggregate a candidate, strategy, or portfolio across explicit scenarios under a declared robust-decision rule such as worst-case score, minimax regret, maximin, threshold survival, or pivot-trigger analysis.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill evaluate-scenario-robustness --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/evaluate-scenario-robustness

Context preview

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

Aggregate a candidate, strategy, or portfolio across explicit scenarios under a declared robust-decision rule such as worst-case score, minimax regret, maximin, threshold survival, or pivot-trigger analysis.

SKILL.md

evaluate-scenario-robustness.SKILL.md
name: evaluate-scenario-robustness
description: "Aggregate a candidate, strategy, or portfolio across explicit scenarios under a declared robust-decision rule such as worst-case score, minimax regret, maximin, threshold survival, or pivot-trigger analysis."

evaluate-scenario-robustness

Purpose

Aggregate candidate performance across explicit scenarios under a declared robust-decision rule.

Input contract

required: [candidate_set, scenario_set, criterion_results, robustness_rule]
optional: [regret_definition, survival_thresholds, pivot_triggers]
constraints: [scenario results use common criteria and direction]

Procedure

1. Verify scenario comparability and criterion direction. 2. Apply the supplied rule: worst-case, minimax regret, maximin, survival, or pivot trigger. 3. Expose scenario-specific failures and tradeoffs. 4. Return ranking, rule sensitivity, and pivot conditions.

Output contract

produces: [robustness_assessment, robust_ranking, regret_or_worst_case, pivot_triggers]
delta_fields: [findings, decisions, uncertainties]

Quality gates

  • At least 3 distinct futures are evaluated when the scenario set is intended to span uncertainty.
  • Rule is declared before aggregation and applied consistently.
  • A candidate failing a survival threshold is not rescued by averaging.

Parameterization

Caller supplies scenario schema, criterion scales, aggregation rule, regret/survival definitions, and pivot policy.

Failure and counterexamples

Reject hidden scenario weighting, incomparable metrics, or robustness claims from a single future.

Provenance map

  • concept: experiment-execution/robustness-scoring
  • concept: experiment-execution/strategy-robustness-testing
  • concept: convergence/portfolio-optimization/robustness-under-uncertainty
  • intermediate: Pass8/score-scenario-robustness
  • intermediate: Pass8/evaluate-regret-robustness
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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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Python
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Apache-2.0
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9h ago
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7mo ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine