abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Produce a normalized criterion-weight vector using a selected elicitation method.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/elicit-weightsContext preview
The summary Claude sees to decide when to auto-load this skill.
Produce a normalized criterion-weight vector using a selected elicitation method.
name: elicit-weights description: "Produce a normalized criterion-weight vector using a selected elicitation method."
Produce a normalized criterion-weight vector using a caller-selected elicitation method.
required: [criteria, elicitation_method, preference_inputs] optional: [consistency_threshold, pairwise_scale, missing_preference_policy] constraints: [weights are nonnegative and sum to 1 within declared tolerance]
1. Validate criteria and method applicability. 2. Convert preference inputs to a weight vector using the declared method. 3. Compute consistency diagnostics where the method supports them. 4. Return weights, diagnostics, and unresolved preference gaps.
produces: [weight_vector, consistency_diagnostics, preference_gaps, method_record] delta_fields: [decisions, uncertainties, open_questions]
Caller supplies criterion schema, method, pairwise/preference scale, tolerance, and consistency policy.
Reject inapplicable dimensionality, negative weights, missing comparisons, or unreported inconsistency.
| source | criterion | |---|---| | hypothesis-formation/ahp-weighting | AHP applicability range is 2-9 dimensions. | | hypothesis-formation/ahp-weighting | Weight vector sums to 1.0 within +/-0.001. | | hypothesis-formation/ahp-weighting | CR > 0.1 is flagged. |
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.