evaluate-scenario-robustness

SkillDev tools

Lets your agent compare options across multiple scenarios and rank them by robustness rules like worst-case or minimax regret.

Available today. Use it from your connected AI after setup.

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 evaluate-scenario-robustness skill

About 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.

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/evaluate-scenario-robustness/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
evaluate-scenario-robustness
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine