abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill identify-load-bearing-factors --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/identify-load-bearing-factorsContext preview
The summary Claude sees to decide when to auto-load this skill.
Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion.
name: identify-load-bearing-factors description: "Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion."
Identify factors or assumptions whose change most strongly controls the conclusion.
required: [conclusion, factor_or_assumption_set, perturbation_evidence] optional: [necessity_sufficiency_results, uncertainty_contributions, critical_path] constraints: [load-bearing status is tied to observed or reasoned conclusion change; preserve factor identity]
1. Assemble ablation, necessity/sufficiency, fragility, and uncertainty evidence. 2. Compare conclusion changes attributable to each factor. 3. Classify factors as necessary, sufficient, jointly necessary, or decorative where supported. 4. Rank load-bearing factors and state the evidence gap for each uncertain ranking.
produces: [load_bearing_register, necessity_sufficiency_map, fragility_ranking, evidence_gaps] delta_fields: [findings, evidence_updates, decisions, uncertainties]
The caller must provide conclusion schema, factor/assumption list, perturbation results, classification ontology, importance metric, and uncertainty representation.
Reject a load-bearing claim based only on correlation, an untested factor, or a perturbation that changes multiple undeclared inputs.
No numeric source gate was present in the resolved source nodes; the role and evidence requirements above preserve their qualitative constraints.
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.