Research Literacy

Plan cognitive science research with hypotheses, assumptions, and human-in-the-loop checkpoints.

34|5|Updated Feb 28, 2026
One-click install
npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill research-literacy-neuroaihub
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Research Literacy
Source: https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills/tree/main/skills/research-literacy
Command: npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill research-literacy-neuroaihub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill forces deliberate research planning to prevent theory-light, assumption-ignorant analytics by requiring question formulation, method justification, and human-in-the-loop checkpoints before initiating cognitive science or neuroscience analyses.

Core Features & Use Cases

  • Research question scaffolding: Uses the PICOS framework and confirmatory/exploratory distinction to turn vague phenomena into falsifiable, operational hypotheses.
  • Method justification and assumption audit: Matches question types to analysis families, documents alternatives, and consults references/common-assumptions.md to plan assumption checks and limitation statements.
  • Human-in-the-loop protocol: Highlights mandatory pause points for exclusions, outliers, model specifications, and unexpected results so the agent always waits for user confirmation before acting.

Quick Start

Ask the assistant to help me map out a confirmatory research plan covering question, hypotheses, assumptions, and limitations before my next cognitive neuroscience analysis.

Frequently Asked Questions about Research Literacy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I justify my analysis methods before running cognitive neuroscience data pipelines?

To justify analysis methods, you must align research planning with hypotheses, document method alternatives, and perform assumption checks before data processing. This approach prevents ad-hoc errors by requiring explicit method justification prior to execution.

What is the difference between confirmatory and exploratory research planning in data analysis?

Confirmatory research planning tests pre-defined hypotheses, while exploratory analysis investigates patterns without prior expectations. Labeling analyses as confirmatory versus exploratory prevents theory-light analytics and ensures transparent reporting of limitations.

How do I use the PICOS framework to build falsifiable hypotheses for research planning?

The PICOS framework scaffolds research questions by breaking down population, intervention, comparison, outcomes, and study design. This structure turns vague phenomena into falsifiable, operational hypotheses for rigorous planning.

How do I implement human-in-the-loop checkpoints for outlier handling and model specification?

Human-in-the-loop checkpoints are implemented by pausing the data pipeline before exclusions, outlier removal, and model specification. The system waits for user confirmation at these mandatory decision points to prevent ad-hoc errors.

When do I need to document assumption checks and limitations in a research plan?

Assumption checks and limitations must be documented during the research planning phase before any data analysis begins. This ensures assumption-ignorant analytics are avoided by consulting common assumption references beforehand.

Can I map analysis families to specific research question types before processing neuroscience data?

Yes, research planning involves matching question types to appropriate analysis families and logging method alternatives. This method justification ensures the selected analytical approach aligns with the operational hypotheses.