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/formulate-research-question

Convert a hypothesis into a precise research question with fit-for-purpose framework, scope, feasibility, and success criteria.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill formulate-research-question --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/formulate-research-question

Context preview

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

Convert a hypothesis into a precise research question with fit-for-purpose framework, scope, feasibility, and success criteria.

SKILL.md

formulate-research-question.SKILL.md
name: formulate-research-question
description: "Convert a hypothesis into a precise research question with fit-for-purpose framework, scope, feasibility, and success criteria."

formulate-research-question

Purpose

Convert a hypothesis into a precise research question with fit-for-purpose framework, scope, feasibility, and success criteria.

Input contract

required: [hypothesis_or_gap, candidate_frameworks, feasibility_constraints]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

1. You MUST load skill `apply-question-framework` to construct the question under the selected framework. 2. You MUST load skill `assess-question-quality` to assess answerability, significance, and precision. 3. You MUST load skill `define-criteria` to define the question-quality criteria. 4. You MUST load skill `set-threshold` to set the acceptance thresholds. 5. You MUST load skill `adjust-abstraction-scope` to correct the abstraction level and scope. If the accepted question contains dependent subproblems, consider `decompose-research-question` as the next tactic.

Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.

Output contract

produces: [research_question, success_criteria, scope_decision]
delta_fields: [decisions, open_questions]

Thresholds and quality gates

  • Each output is traceable to an input object, operation, and evidence reference.
  • Scope, assumptions, and unresolved alternatives remain explicit.
  • Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.

Failure and counterexamples

Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.

Provenance map

  • resolved: research-question
  • resolved: framework-guided-formulation
  • resolved: scope-calibration
  • resolved: comparative-formulation
  • resolved: feasibility-constrained-formulation
  • resolved: framework-selection-and-application
  • resolved: question-refinement-loop

Preserved source criteria ledger

| source | criterion | treatment | |---|---|---| | resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract | | experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable | | experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |

Context checkpoint / Delta notes

Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.

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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
5h ago
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7mo ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine