deep-research-skill

🥈Silver

A 7-phase research system for Claude Code that produces decision-grade, auditable, hallucination-resistant research outputs. Multi-source verification and Graph of Thoughts methodology ensure accuracy. Domain overlays for healthcare, financial, legal, and market research. Ideal for operations teams needing reliable, in-depth research.

1110Updated 1mo ago

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/standardhuman/deep-research-skill.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/standardhuman/deep-research-skill

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Perform deep research on [TOPIC] within the [INDUSTRY] sector. Use the 7-phase research system with multi-source verification and Graph of Thoughts methodology. Apply the [DOMAIN] overlay (healthcare, financial, legal, or market research) to ensure accuracy. Provide decision-grade outputs with clear citations.

Example Output

# Deep Research on AI Adoption in Healthcare

## Key Findings
- **Current Adoption Rates**: AI is being adopted at a rate of 35% among major healthcare providers, with a projected increase to 60% by 2025.
- **Key Drivers**: The primary drivers include cost reduction (45%), improved diagnostic accuracy (30%), and operational efficiency (25%).
- **Challenges**: Data privacy concerns (50%), regulatory hurdles (30%), and integration with existing systems (20%) are the main challenges.

## Multi-Source Verification
- **Source 1**: Healthcare AI Adoption Report 2023, Journal of Medical Informatics
- **Source 2**: AI in Healthcare Market Analysis, McKinsey & Company
- **Source 3**: Regulatory Impact Study, FDA

## Graph of Thoughts Analysis
1. **Initial Hypothesis**: AI adoption is driven by cost savings.
2. **Verification**: Confirmed by multiple sources, but diagnostic accuracy is also a significant factor.
3. **Conclusion**: AI adoption is multifaceted, with cost savings being the primary but not sole driver.

## Recommendations
- **Immediate Actions**: Address data privacy concerns through robust encryption and compliance measures.
- **Long-Term Strategy**: Invest in AI integration frameworks to streamline adoption.