Research Workflow Optimizer
0This skill streamlines research by integrating Perplexity for real-time data collection and NotebookLM for in-depth analysis, enabling users to efficiently gather, organize, and analyze information for various projects.
000Updated 3w ago
Advanced1 hour to implementAI Development
Saves ~240 min per use
Quick Install
claude skill add research-workflow-optimizer-mkug4foqWorks with:
Claude CodeCursorWindsurf
Overview
About This Skill
The Research Workflow Optimizer is an advanced Claude Code skill designed to enhance the efficiency of research processes. By integrating Perplexity for real-time data collection and NotebookLM for comprehensive analysis, this skill allows users to gather, organize, and analyze information seamlessly. This streamlined approach is particularly beneficial for developers, product managers, and AI practitioners who require quick access to reliable data for their projects. One of the key benefits of the Research Workflow Optimizer is its ability to save time by automating the data collection and analysis phases of research. Although specific time savings are not quantified, the integration of these powerful tools significantly reduces the manual effort involved in gathering and processing information. Users can expect to complete research tasks more swiftly, allowing them to focus on strategic decision-making and project execution. This skill is ideal for professionals engaged in market trend analysis, product enhancement research, and audience research. For instance, product managers can leverage this skill to analyze competitive data, identifying opportunities for product improvements. Similarly, marketers can utilize it to craft targeted messages based on audience insights, ensuring their campaigns resonate with the intended demographic. The Research Workflow Optimizer is particularly useful for teams looking to adopt an AI-first approach, where data-driven decision-making is paramount. With an intermediate implementation difficulty, users can expect to set up the Research Workflow Optimizer in about one hour. This skill is designed to fit seamlessly into existing workflows, enhancing productivity without requiring extensive technical expertise. By incorporating this AI automation skill into your research processes, you can unlock new levels of efficiency and insight, propelling your projects forward in today's fast-paced environment.
How to Use
1. In Perplexity, set up a space for your research topic and gather quality sources like reports and articles. 2. Import these sources into NotebookLM. 3. Use the provided questions to guide your analysis and extract actionable insights. 4. Optionally, validate insights with additional data from Perplexity.
Use Cases
Market trend analysis to plan content and leadership pieces
Product enhancement research by analyzing competitive data
Audience research to craft targeted marketing messages
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Requirements
- Claude Code or compatible AI agent
- Works with: Claude Code, Cursor, Windsurf
- Advanced skill - requires experience with AI agents
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
Use Perplexity to gather sources by creating a space and setting up custom instructions. Collect relevant links such as reports, articles, or videos. Then, import these sources into NotebookLM for deep analysis. In NotebookLM, ask the following questions to extract insights: 1. What are the major trends or patterns? 2. What are the primary challenges or pain points? 3. Propose content ideas or improvements. Optional: Validate findings by cross-checking with open web data using Perplexity.
Example Output
Using NotebookLM to analyze sources on Responsible AI, the AI identifies trends such as increased adoption of Human-in-the-Loop Oversight and highlights gaps in current market offerings, suggesting potential areas for product development.
