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Skill

/construct-scenario

Construct a scenario across key uncertainties with explicit assumptions and plausibility. Modes include baseline/narrative, counterfactual, and extreme-but-plausible worst-case with breaking point, failure cascade, recovery assumptions, and distinguishing observables.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-scenario --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/construct-scenario

Context preview

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

Construct a scenario across key uncertainties with explicit assumptions and plausibility. Modes include baseline/narrative, counterfactual, and extreme-but-plausible worst-case with breaking point, failure cascade, recovery assumptions, and distinguishing observables.

SKILL.md

construct-scenario.SKILL.md
name: construct-scenario
description: "Construct a scenario across key uncertainties with explicit assumptions and plausibility. Modes include baseline/narrative, counterfactual, and extreme-but-plausible worst-case with breaking point, failure cascade, recovery assumptions, and distinguishing observables."

construct-scenario

Purpose

Construct scenarios across key uncertainties with explicit assumptions and plausibility, including baseline, counterfactual, and extreme-but-plausible modes.

Input contract

required: [context, uncertainty_set, scenario_mode]
optional: [drivers, dependency_model, probability_method, recovery_assumptions]
constraints: [scenario assumptions and distinguishing observables must be explicit]

Procedure

1. Select uncertainty axes and record their state space. 2. Generate distinct combinations appropriate to the mode. 3. Propagate interactions, breaking points, failure cascades, and recovery assumptions where applicable. 4. Attach plausibility rationale, distinguishing observables, and outcome implications.

Output contract

produces: [scenario_set, assumptions, plausibility_rationales, observables, outcome_implications]
delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]

Quality gates

  • At least 3 scenarios span different combinations of key uncertainties.
  • Scenarios are internally consistent and materially distinct.
  • Worst-case mode includes breaking point, cascade, recovery, and observables.

Parameterization

Caller supplies context schema, uncertainty axes, mode, distinctness rule, plausibility/probability method, and worst-case fields.

Failure and counterexamples

Reject decorative narratives that do not vary an uncertainty or expose a testable implication.

Provenance map

  • resolved: scenario-construction
  • resolved: counterfactual-scenario-construction
  • resolved: worst-case-construction
  • resolved: stress-scenario

Preserved source criteria ledger

| source | criterion | |---|---| | scenario-construction | Output contains at least 3 distinct scenarios spanning different combinations of key uncertainties. | | scenario-construction | Each scenario includes a narrative, key assumptions, and probability estimate. | | worst-case-construction | Extreme-but-plausible mode includes breaking points, failure cascades, and recovery assessment. |

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Ships withde-anthropocentric-research-engine

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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