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deep-research

Orchestrate an 8-phase research pipeline with Python scripts for verified, citation-backed reports.

4|Updated Feb 14, 2026
Search Tags:#research#verification#analysis#reporting#synthesis#knowledge discovery
One-click install
npx skills add https://github.com/KuaaMU/omnihive --skill deep-research-kuaamu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/KuaaMU/omnihive/tree/main/library/real-skills/deep-research
Command: npx skills add https://github.com/KuaaMU/omnihive --skill deep-research-kuaamu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of conducting in-depth, multi-source research, overcoming the limitations of simple web searches by providing verified, synthesized, and actionable reports.

Core Features & Use Cases

  • Comprehensive Analysis: Conducts deep dives into complex topics, comparisons, and trend analyses.
  • Source Verification: Triangulates information from 10+ sources, scores credibility, and flags contradictions.
  • Actionable Reports: Delivers structured reports with executive summaries, detailed findings, insights, and recommendations.
  • Use Case: Use this Skill to research the competitive landscape for a new product, analyze the feasibility of a technical approach, or understand the latest scientific breakthroughs.

Quick Start

Use deep research to analyze the current state of AI agent frameworks in 2025.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I conduct deep research with automated source verification and synthesis?▼

Deep research automates source verification by triangulating information from 10+ sources, scoring credibility, flagging contradictions, and synthesizing findings into structured, citation-backed reports.

What is the best way to generate a verified competitive landscape analysis report?▼

Generating a competitive landscape analysis is best handled by running an automated 8-phase research pipeline that executes parallel information retrieval, critique, and refinement to package verified findings into actionable reports.

Can I use Python scripts to automate research reporting and citation management?▼

Yes, you can use Python scripts to automate research reporting execution, validation, and citation management, ensuring your synthesized analysis is properly verified and packaged.

Does automated source triangulation work for complex technical feasibility analysis?▼

Automated source triangulation works for technical feasibility analysis by scoring credibility across multiple sources, flagging contradictions, and refining insights through an 8-phase pipeline into an actionable report.

How does the 8-phase research pipeline handle contradictions found during knowledge discovery?▼

The 8-phase research pipeline handles contradictions during knowledge discovery by scoring source credibility, flagging conflicting information, and applying critique and refinement phases before final synthesis.

When should I avoid using an automated research pipeline for information retrieval?▼

You should avoid using an automated research pipeline when your task requires simple web search queries rather than comprehensive multi-source synthesis, or when your topic lacks sufficient credible sources for triangulation.

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