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Automation
Skill

/assess-question-quality

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

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill assess-question-quality --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/assess-question-quality

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

assess-question-quality.SKILL.md
name: assess-question-quality
description: "Audit a research question for scope and FINER-style quality; return targeted repairs."

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`
Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Python
Language
Apache-2.0
License
2h ago
Last commit
7mo ago
Created

Repo: yogsoth-ai/de-anthropocentric-research-engine