Claude-Code-Deep-Research-main

🥈Silver

Automates deep research using Claude Code's agent framework. Benefits operations teams by streamlining complex research tasks. Connects to Claude Code's Skills and Commands system for efficient data gathering and analysis.

236270Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/liangdabiao/Claude-Code-Deep-Research-main.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Install Claude Code:** Ensure you have [Claude Code](https://github.com/anthropics/claude-code) installed and authenticated with API access. Run `claude --version` to confirm installation. 2. **Define Scope:** Replace [TOPIC] with your research goal (e.g., "The future of quantum computing in healthcare"). Specify [SOURCES] (e.g., "PubMed, IEEE Xplore, arXiv"). 3. **Run the Command:** Execute the prompt in your terminal or IDE: ``` claude "Use Claude Code to automate deep research on [TOPIC]. Follow these steps: 1) Identify 5-7 key subtopics/questions to explore. 2) Gather data from [SOURCES] (prioritize official docs, peer-reviewed papers, or primary sources). 3) Analyze findings for patterns, contradictions, or gaps. 4) Synthesize into a structured report with bullet points, tables, or summaries. 5) Flag any unresolved questions or areas requiring further investigation. Sources must be cited with direct links." ``` 4. **Refine Output:** Review the generated report. Use the unresolved questions to guide further research or manual verification. For large datasets, export results to CSV/JSON for deeper analysis in tools like Excel or Python. 5. **Iterate:** Adjust [SOURCES] or subtopics based on gaps identified in the first run. For example, if regulatory impacts are missing, add "[REGULATORY BODIES]" to the sources list. **Tips:** - Use `claude --max-turns 10` to limit research depth if time-sensitive. - For technical topics, include "peer-reviewed papers" in [SOURCES] to filter low-quality results. - If the output is too broad, narrow the scope by adding constraints (e.g., "focus on 2022-2024 data").

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/liangdabiao/Claude-Code-Deep-Research-main

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

Use Claude Code to automate deep research on [TOPIC]. Follow these steps: 1) Identify 5-7 key subtopics/questions to explore. 2) Gather data from [SOURCES] (prioritize official docs, peer-reviewed papers, or primary sources). 3) Analyze findings for patterns, contradictions, or gaps. 4) Synthesize into a structured report with bullet points, tables, or summaries. 5) Flag any unresolved questions or areas requiring further investigation. Sources must be cited with direct links. [TOPIC] = [PLACEHOLDER]

Example Output

Deep Research Report: 'The Impact of AI on Cybersecurity Workforce Skills'

**Key Subtopics Explored:**
1. AI-driven threat detection tools (2023 market leaders)
2. Skill gaps in cybersecurity teams (2024 surveys)
3. AI's role in automating SOC tasks
4. Regulatory impacts (NIST, ISO 27001 updates)
5. Cost-benefit analysis of AI adoption in cybersecurity

**Data Gathering Results:**
- **Threat Detection:** Palo Alto Cortex XDR (2023) reduced mean time to detect (MTTD) by 45% vs. traditional SIEMs (source: [Palo Alto 2023 Report](https://www.paloaltonetworks.com/resources)). Darktrace’s autonomous response cut incident response time by 60% (source: [Darktrace 2024 Case Study](https://darktrace.com/resources)).
- **Skill Gaps:** 68% of cybersecurity professionals lack AI/ML expertise (ISC² 2024 Workforce Study). 42% of organizations cite "AI literacy" as their top hiring challenge (source: [ISC² 2024 Report](https://www.isc2.org/Research)).
- **Regulatory:** NIST SP 800-207 (Zero Trust Architecture) now includes AI-driven identity verification requirements (source: [NIST.gov](https://csrc.nist.gov)). ISO 27001:2022 mandates AI risk assessments for cloud services.

**Key Findings:**
1. **AI Adoption Accelerates:** 72% of enterprises plan to increase AI investment in cybersecurity by 2025 (Gartner 2024).
2. **Skill Shortage Critical:** Only 18% of cybersecurity roles require AI/ML expertise (LinkedIn 2024 Data).
3. **Regulatory Pressure:** Organizations must align with NIST/ISO frameworks or face compliance risks.

**Unresolved Questions:**
- How do SMEs (small/medium enterprises) balance AI adoption with budget constraints?
- What are the long-term ethical implications of AI-driven cybersecurity decisions?

**Recommendations:**
- Prioritize upskilling in AI/ML for SOC teams (e.g., SANS SEC595 course).
- Pilot AI tools in non-critical environments to measure ROI before full deployment.

*Sources compiled on [DATE]. All links verified and accessible as of [DATE].*