Datagen Research Guide

• 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
• Generates analysis reports with effect sizes and confidence intervals

Writing Agent

• Drafts sections following target journal formatting guidelines
• Integrates results from analysis into coherent narratives
• Ensures claims are proportional to the evidence strength
• Manages references and in-text citations consistently
• Produces abstracts, summaries, and highlight points

Pipeline Orchestration

Coordinating multiple agents requires careful orchestration:

Task Decomposition

• Break the overall research question into sub-tasks aligned with agent capabilities
• Define clear input-output contracts between agents
• Establish quality gates at each pipeline stage
• Allow for iterative refinement when downstream agents identify issues
• Maintain a shared context document accessible to all agents

Quality Control

• Each agent output passes through a validation checkpoint
• Cross-reference literature findings with known databases
• Verify statistical analyses meet the assumptions of chosen tests
• Check written outputs against reporting guidelines (APA, CONSORT, etc.)
• Flag inconsistencies between sections for human review

Error Recovery

• Define fallback strategies when an agent cannot complete its task
• Allow agents to request clarification from upstream agents
• Implement retry logic with modified parameters for failed steps
• Escalate to human oversight when confidence is below threshold
• Log all decisions and their rationale for audit trails

Data Generation Workflows

The DATAGEN approach excels at synthetic data generation for research:

• Generate synthetic datasets matching real-world statistical properties
• Create simulation-based datasets for power analysis and method testing
• Produce augmented training data for machine learning experiments
• Build synthetic control groups when ethical constraints limit real data
• Validate analysis pipelines on known ground truth before applying to real data

Research Domain Applications

This skill adapts to multiple research contexts:

Social Sciences - Survey design, factor analysis, structural equation modeling Natural Sciences - Experimental protocols, measurement validation, replication studies Computer Science - Benchmark design, ablation studies, performance evaluation Health Sciences - Clinical trial design, meta-analysis, systematic reviews Engineering - Design of experiments, optimization, reliability testing

Integration with Research-Claw

This skill coordinates with other Research-Claw capabilities:

• Literature search skills feed the Literature Agent
• Statistical analysis skills power the Analysis Agent
• Writing and citation skills support the Writing Agent
• Domain-specific skills provide specialized knowledge to all agents
• The orchestration layer uses Research-Claw's task management for pipeline control

Best Practices

• Always maintain human oversight at critical decision points
• Document every automated decision with its reasoning
• Validate automated outputs against domain expert judgment periodically
• Start with simpler single-agent workflows before scaling to multi-agent pipelines
• Use version control for all generated artifacts (data, analyses, drafts)
• Ensure reproducibility by logging all random seeds and model versions