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Skill

/elicit-weights

Produce a normalized criterion-weight vector using a selected elicitation method.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights --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/elicit-weights

Context preview

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

Produce a normalized criterion-weight vector using a selected elicitation method.

SKILL.md

elicit-weights.SKILL.md
name: elicit-weights
description: "Produce a normalized criterion-weight vector using a selected elicitation method."

elicit-weights

Purpose

Produce a normalized criterion-weight vector using a caller-selected elicitation method.

Input contract

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]

Procedure

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.

Output contract

produces: [weight_vector, consistency_diagnostics, preference_gaps, method_record]
delta_fields: [decisions, uncertainties, open_questions]

Quality gates

  • AHP-style elicitation accepts 2-9 dimensions only.
  • Weight sum is 1.0 within caller-declared tolerance (default +/-0.001).
  • Consistency ratio is reported; CR > 0.1 is flagged when applicable.

Parameterization

Caller supplies criterion schema, method, pairwise/preference scale, tolerance, and consistency policy.

Failure and counterexamples

Reject inapplicable dimensionality, negative weights, missing comparisons, or unreported inconsistency.

Provenance map

  • concept: hypothesis-formation/ahp-weighting
  • concept: convergence/weight-elicitation-sop

Preserved source criteria ledger

| 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. |

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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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Repo: yogsoth-ai/de-anthropocentric-research-engine