define-criteria
SkillDev toolsLets your agent turn a research goal into clear, measurable criteria for comparing options.
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 define-criteria skill
About this skill
Derive explicit evaluation criteria from the research objective and candidate set.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/define-criteria/SKILL.md and read by ahel’s review.
Purpose
Derive explicit evaluation criteria from the research objective and candidate set.
Input contract
required: [research_objective, candidate_set, decision_context]
optional: [stakeholder_priorities, measurement_constraints, candidate_domains]
constraints: [criteria must be mutually interpretable across candidates]
Procedure
- Extract desired outcomes and constraints from the objective.
- Translate them into candidate-discriminating criteria with definitions and units.
- Check completeness, overlap, direction, and measurability.
- Return the criterion schema and unresolved measurement questions.
Output contract
produces: [criterion_schema, measurement_definitions, direction_labels, coverage_notes]
delta_fields: [findings, open_questions, uncertainties]
Quality gates
- Criteria count is between 3 and 12 unless caller explicitly authorizes another range.
- Every criterion has name, definition, unit, and higher/lower-is-better direction.
- Criteria are non-overlapping enough that double counting is documented.
Parameterization
Caller supplies objective schema, candidate schema, criterion count bounds, measurement units, direction vocabulary, and overlap policy.
Failure and counterexamples
Reject vague criteria lacking an observable measurement or criteria that cannot distinguish any candidate.
Provenance map
- resolved: criterion-definition
- concept: hypothesis-formation/scoring-matrix-construction (criteria-extraction core)
- resolved: hypothesis-formation-scoring-matrix-construction
- intermediate: Pass4/define-success-criteria
Preserved source criteria ledger
| source | criterion |
|---|---|
| criterion-definition | Criteria count is between 3-12. |
| criterion-definition | Each criterion includes name, definition, unit of measurement, and direction (higher-is-better/lower-is-better). |
| convergence-scoring-matrix-construction | Normalization method matches the aggregation method. |
| convergence-scoring-matrix-construction | Sensitivity testing perturbs at least 3 weight parameters by +/-10%. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
define-criteria- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine