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deep-research

Execute an 8-phase research pipeline and generate citation-backed reports.

15|5|Updated Dec 5, 2025
Search Tags:#research#verification#analysis#reporting#citations#data analysis#synthesis
One-click install
npx skills add https://github.com/rebyteai-template/rebyte-skills --skill deep-research-rebyteai-template
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/rebyteai-template/rebyte-skills/tree/main/deep-research
Command: npx skills add https://github.com/rebyteai-template/rebyte-skills --skill deep-research-rebyteai-template

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process of conducting in-depth research, synthesizing information from multiple sources, and generating comprehensive, citation-backed reports, overcoming the limitations of basic web searches and manual analysis.

Core Features & Use Cases

  • Comprehensive Research: Conducts multi-phase research (Scope, Plan, Retrieve, Triangulate, Synthesize, Critique, Refine, Package) for deep understanding.
  • Source Verification: Validates claims across multiple sources, scores source credibility, and flags contradictions.
  • Automated Reporting: Generates professional Markdown, HTML (McKinsey-style), and PDF reports with full citations and methodology.
  • Use Case: Use this Skill to analyze complex technical topics, compare competing products, or generate thorough market trend reports that require verified data and expert-level synthesis.

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 automate deep research and generate a verified report with citations?▼

Automate deep research by executing an 8-phase pipeline that retrieves sources, triangulates data, and synthesizes findings to generate citation-backed reports in Markdown, HTML, and PDF formats.

What is source triangulation and how does it verify research data?▼

Source triangulation validates claims across multiple sources to ensure research accuracy, scores source credibility, and flags contradictions to verify data integrity during the synthesis phase.

Can I generate a McKinsey-style HTML report for market trend analysis?▼

Generate McKinsey-style HTML reports for market trend analysis through automated report packaging, which outputs professional documents complete with full citations and methodology in Markdown, HTML, and PDF formats.

How do I conduct multi-source verification for complex technical topics?▼

Conduct multi-source verification for complex technical topics by running an automated pipeline that retrieves data, scores source credibility, and critiques findings to handle queries requiring expert-level synthesis.

Does deep research require external dependencies to run Python scripts?▼

No external dependencies are required to run deep research Python scripts, as the skill orchestrates validation and retrieval internally while adhering to context engineering best practices for efficiency.

What is the best way to synthesize competing product data into a structured format?▼

Synthesize competing product data into structured formats by using the automated pipeline to retrieve, triangulate, critique, and package findings into Markdown, HTML, or PDF reports with full citations.

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