Deep Research Pro

A professional-grade deep research methodology that coordinates multi-source information gathering, cross-reference verification, and structured synthesis. Designed for research tasks that require high confidence in factual accuracy, comprehensive coverage, and traceable evidence chains.

Overview

Deep Research Pro implements an agent-based methodology where the research process is decomposed into specialized phases: query decomposition, parallel source gathering, cross-reference verification, contradiction resolution, and structured synthesis. Unlike simple search-and-summarize approaches, this skill emphasizes source triangulation, evidence grading, and explicit uncertainty marking.

The methodology is particularly valuable for literature reviews, technology assessments, policy analyses, and any research task where decision-makers need to trust the completeness and accuracy of the findings. Every claim in the final output is linked to at least one verified source, with confidence levels assigned based on the quality and agreement of the evidence.

Research Agent Architecture

Phase 1: Query Decomposition

def decompose_research_query(query: str) -> dict:
    """
    Break a complex research question into atomic sub-questions
    that can be independently investigated.