elicit-weights
SkillDev toolsLets your agent turn your stated preferences into normalized weights for a set of decision criteria.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the elicit-weights skill
About this skill
Produce a normalized criterion-weight vector using a selected elicitation method.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/elicit-weights/SKILL.md and read by ahel’s review.
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
- Validate criteria and method applicability.
- Convert preference inputs to a weight vector using the declared method.
- Compute consistency diagnostics where the method supports them.
- 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. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
elicit-weights- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine