DATAGEN Research Guide

DATAGEN Research Guide

A skill for orchestrating AI-driven multi-agent research workflows that handle literature review, hypothesis generation, experiment design, data analysis, and report writing. Based on the DATAGEN project (2K stars), this skill provides structured guidance on building automated research pipelines using collaborative agent architectures.

Overview

Modern research increasingly benefits from AI assistance at every stage. DATAGEN's approach uses multiple specialized agents that collaborate on a research task, each handling a different aspect of the workflow. This skill teaches the agent how to coordinate such multi-agent pipelines, ensuring quality control at each handoff point and maintaining scientific rigor throughout.

The multi-agent paradigm is particularly powerful for research tasks that span multiple competencies: a literature agent gathers relevant prior work, a methodology agent designs appropriate experiments, a data agent handles collection and cleaning, an analysis agent runs statistical tests, and a writing agent produces publication-ready text.

Multi-Agent Architecture

The research pipeline employs these specialized agent roles:

Literature Agent

• Conducts systematic literature searches across academic databases
• Filters results by relevance, recency, and citation impact
• Extracts key findings and methodological details from selected papers
• Identifies research gaps that motivate the current study
• Produces structured literature summaries with citation metadata

Hypothesis Agent

• Generates testable hypotheses based on literature gaps
• Evaluates feasibility of proposed hypotheses given available resources
• Ranks hypotheses by potential impact and testability
• Defines operationalizations for abstract constructs
• Produces formal hypothesis statements with predicted effect directions

Experiment Agent

• Designs experimental protocols appropriate to the hypotheses
• Selects control conditions and randomization strategies
• Calculates sample size requirements and power estimates
• Identifies potential confounds and proposes mitigation strategies
• Generates detailed protocol documents suitable for pre-registration

Analysis Agent

• Selects statistical methods aligned with the experimental design
• Implements analysis pipelines with documented parameters
• Runs assumption checks before applying parametric tests
• Produces visualization of results with appropriate uncertainty measures