abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
@yogsoth-aiInstall SkillAutomation
Skill
/elicit-weights
Produce a normalized criterion-weight vector using a selected elicitation method.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill elicit-weights -a windsurf -y
SKILL.md
elicit-weights.SKILL.mdname: 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. |
Read more
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. |
Ships withde-anthropocentric-research-engine
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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Questions about elicit-weights.
- Which coding agents can use elicit-weights?
- elicit-weights installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.