What problem does it solve? Rigorous research requires coordinating literature search, evidence extraction, code-based data analysis, and report writing — a workflow that is error-prone and hard to keep consistent when done ad hoc. This Skill automates that orchestration by dispatching specialized sub-agents per domain, enforcing iteration and validation rules, and assembling traceable final reports. ## Core Features & Use Cases - Domain-based orchestration: Parses the research question to activate knowledge (literature-searcher, evidence-extractor), data (code-engineer, result-evaluator), and report (report-writer) domains, running knowledge first when both are needed. - Evaluate-and-revise loop: Runs code-engineer and result-evaluator in an iteration loop capped by max_engineer_evaluator_iterations, passing revision guidance verbatim between rounds. - Traceable report synthesis: Integrates Knowledge and Analysis Summaries with strict schema and provenance rules, then dispatches report-writer for final deliverables. - Use Case: Ask a question like "analyze whether mRNA vaccine efficacy varies by age group using my trial CSV and recent literature" — the skill searches and extracts evidence, runs the statistical analysis with evaluation rounds, and delivers a synthesized report. ## Quick Start Use the science-research-team skill to research recent advances in mRNA vaccines and analyze my trial_results.csv dataset, then produce a combined report.
