yogsoth-ai/de-anthropocentric-research-engine

formulate-research-question

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

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill formulate-research-question

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

formulate-research-question

Purpose

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

Input contract

yaml
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

yaml
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

sourcecriteriontreatment
resolved v3 entries abovenode-specific criteriaretained and specialized to the v4 object contract
experiment-execution/statistical-testing$\alpha$ = 0.05fixed value retained where applicable
experiment-execution/sample-size-estimationpower = 0.8fixed 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.

Correlati per attività

dallo stesso repository

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