assess-question-quality
SkillDev toolsLets your agent audit a research question for clarity, focus, and scope, then suggest targeted wording fixes.
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 assess-question-quality skill
About this skill
Audit a research question for scope and FINER-style quality; return targeted repairs.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/assess-question-quality/SKILL.md and read by ahel’s review.
Purpose
Assess whether a research question is clear, focused, answerable, relevant, and appropriately scoped.
Input contract
required: [research_question, intended_use]
optional: [constraints, evidence_context, stakeholder_needs]
constraints: [each quality judgment requires a criterion and textual evidence]
Procedure
- Evaluate clarity, focus, answerability, relevance, and scope fit.
- Identify ambiguity, hidden assumptions, and unbounded terms.
- Propose the smallest wording changes that resolve material defects.
- Return a scored or categorical assessment with unresolved issues.
Output contract
produces: [quality_assessment, criterion_evidence, revision_candidates, unresolved_issues]
delta_fields: [findings, decisions, uncertainties, open_questions]
Quality gates
- Judgments cite exact question terms.
- Revision candidates preserve the intended decision or outcome.
Failure and counterexamples
Do not reward complexity as rigor or call a question answerable when its outcome cannot be observed.
Provenance map
resolved: assess-question-quality
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
assess-question-quality- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine