abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-scenario --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/construct-scenarioContext 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.
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 scenarios across key uncertainties with explicit assumptions and plausibility, including baseline, counterfactual, and extreme-but-plausible modes.
required: [context, uncertainty_set, scenario_mode] optional: [drivers, dependency_model, probability_method, recovery_assumptions] constraints: [scenario assumptions and distinguishing observables must be explicit]
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.
produces: [scenario_set, assumptions, plausibility_rationales, observables, outcome_implications] delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]
Caller supplies context schema, uncertainty axes, mode, distinctness rule, plausibility/probability method, and worst-case fields.
Reject decorative narratives that do not vary an uncertainty or expose a testable implication.
| 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. |
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Evaluate competing arguments against stated criteria and produce a reasoned verdict with uncertainty.
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context, outcome, timeframe, system…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth,…
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.