evaluate-scenario-robustness
SkillDev toolsLets 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.
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 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
- Verify scenario comparability and criterion direction.
- Apply the supplied rule: worst-case, minimax regret, maximin, survival, or pivot trigger.
- Expose scenario-specific failures and tradeoffs.
- 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