agentleFS
Sign inStart free
yogsoth-aiyogsoth-ai/de-anthropocentric-research-engine/skills/assess-question-quality/SKILL.md

assess-question-quality

Skill505 starsChanged 50 days agoApache-2.0View on GitHub

Audit a research question for scope and FINER-style quality; return targeted repairs.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not reward complexity as rigor or call a question answerable when its outcome cannot be observed.

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

assess-question-quality

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

  1. Evaluate clarity, focus, answerability, relevance, and scope fit.
  2. Identify ambiguity, hidden assumptions, and unbounded terms.
  3. Propose the smallest wording changes that resolve material defects.
  4. 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

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free