How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill assess-question-qualityBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
assess-question-quality
Purpose
Assess whether a research question is clear, focused, answerable, relevant, and appropriately scoped.
Input contract
yaml
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
yaml
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
