deep-research-web-ui
🥇GoldThe deep-research-web-ui is an AI-powered research assistant that conducts in-depth research on any topic by leveraging search engines, web scraping, and large language models. Ideal for users seeking comprehensive insights and data-driven conclusions.
2,1492970Updated 3w ago
Intermediate30 minutes to implementresearch
Saves ~300 min per use
Quick InstallView Source
claude install AnotiaWang/deep-research-web-uiWorks with:
ChatGPT
Overview
About This Skill
https://github.com/AnotiaWang/deep-research-web-ui
Use Cases
Conduct extensive market research for new product launches to identify potential customer segments.
Gather competitive analysis data to understand market positioning and strategies of competitors.
Compile academic literature reviews efficiently by automating the search and extraction of relevant papers.
Perform trend analysis across various industries to identify emerging patterns and opportunities.
Best For
MarketingGrowth
Tags
Setup & Installation
Quick Install
Terminal
claude install AnotiaWang/deep-research-web-uiAlternative Install (Git Clone)
git clone https://github.com/AnotiaWang/deep-research-web-uiRequirements
- Claude Code or compatible AI agent
- Works with: ChatGPT
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Act as a research assistant for [COMPANY], a [INDUSTRY] company. Conduct deep research on [TOPIC]. Provide a comprehensive analysis with data from multiple sources, including recent studies, industry reports, and expert opinions. Focus on [SPECIFIC ASPECT] of the topic. Present the findings in a structured format with clear headings and bullet points.
Example Output
# Research Report: The Impact of AI on the Retail Industry ## Executive Summary The integration of AI in the retail sector has significantly transformed operations, customer experiences, and business strategies. This report provides an in-depth analysis of the current trends, benefits, and challenges associated with AI adoption in retail. ## Key Findings - **Operational Efficiency**: AI-driven automation has reduced operational costs by up to 30% in leading retail companies. - **Customer Experience**: Personalized recommendations powered by AI have increased customer satisfaction by 40%. - **Challenges**: Data privacy concerns and the need for skilled personnel remain significant hurdles. ## Detailed Analysis ### Trends in AI Adoption - **Automation**: AI is widely used for inventory management, supply chain optimization, and customer service automation. - **Personalization**: AI algorithms analyze customer data to provide tailored product recommendations. - **Predictive Analytics**: Retailers use AI to forecast demand and optimize pricing strategies. ### Benefits of AI in Retail - **Cost Reduction**: Automation reduces labor costs and minimizes errors. - **Enhanced Customer Experience**: Personalized interactions increase customer loyalty. - **Data-Driven Decisions**: AI provides actionable insights for strategic planning. ### Challenges and Solutions - **Data Privacy**: Implement robust data protection measures and comply with regulations. - **Skill Gaps**: Invest in training programs for employees to adapt to AI technologies. ## Conclusion AI is revolutionizing the retail industry by enhancing efficiency, improving customer experiences, and enabling data-driven decision-making. However, addressing challenges related to data privacy and skill gaps is crucial for sustained success.
