HackAging AI Case Studies

A community-based project using AI to build a comprehensive database of AI applications in longevity research and aging intervention.

The interactive database interface is available at https://hackaging.ai/research

Project Overview

This repository contains structured case studies of AI applications in longevity research. Each case is stored as a Markdown file with YAML frontmatter for easy editing and automated processing.

Current Status:

• 60 case studies (32 commercial companies, 28 research projects)
• Full information per case: organizations, products, people, finances, events, partnerships
• AI agents have collected and structured the initial data
• Next steps for the community: Validate existing cases and find new case studies

Repository Structure

community-repo/
├── README.md              # Project overview and Cursor instructions
├── YAML_FIELDS.md         # Complete documentation of case structure and required fields
├── cases/                 # All documented case studies
│   ├── commercial/        # Commercial companies (32 cases)
│   └── research/          # Research projects (28 cases)
├── memory-bank/           # Project context and documentation
│   ├── projectbrief.md    # Project overview and case list
│   └── productContext.md  # Project goals and workflows
└── prompts/               # Example prompts for AI agents
    ├── add-new-case.md    # Prompt for adding new cases
    ├── enrich-case.md     # Prompt for enriching existing cases
    └── find-new-cases.md  # Prompt for finding new cases

Key Files:

• cases/ - All case studies stored as Markdown files with YAML frontmatter
• YAML_FIELDS.md - Reference documentation for case structure and all available fields
• memory-bank/ - Project context, goals, and detailed workflows
• prompts/ - Ready-to-use prompts for AI agents working with cases

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Contributing

You can contribute to this project in any way you prefer (Pull Requests, Issues, Discussions). However, we highly recommend using Cursor with Planning Mode for the best experience when working with case studies.

Working with Cursor

Why Cursor?

• AI-powered assistance for finding, reviewing, and improving cases
• Interactive planning mode guides you through workflows
• Automatic validation and structure checking
• Seamless integration with the repository structure

Quick Start:

1. Download Cursor: https://cursor.sh

2. Open this project in Cursor:

   Option A: Clone Repository (Recommended)

   git clone https://github.com/HackAging/AI-Case-Studies
   cd AI-Case-Studies

   Then open the AI-Case-Studies folder in Cursor.

   Option B: Download ZIP (For beginners)

   • Go to https://github.com/HackAging/AI-Case-Studies
   • Click "Code" → "Download ZIP"
   • Extract the ZIP file to your desired location
   • Open the extracted folder in Cursor

3. Start working:

   • Copy the content of this README.md into a new Cursor chat
   • Enable Planning Mode
   • Follow the AI assistant's instructions below

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Cursor Planning Mode: AI Assistant Instructions

You are an AI assistant helping to build a database of AI applications in longevity research. This is a community-based project where we use AI to collect, structure, and maintain case studies.

Your Role

You work in Planning Mode. When a user starts a conversation:

1. First, ask what they want to do:

   • "What would you like to do?"
   • "Would you like to find and document a new case, or review and improve an existing case?"

2. Based on their choice, proceed with the appropriate workflow below.

Workflow 1: Find and Document New Case

When user chooses to find a new case:

1. Ask for case name:

   • "Please provide the name of the company or research project you want to document."

2. Research the case:

   • Use web search to find information
   • Check scientific databases (PubMed, arXiv, bioRxiv)
   • Visit company/research websites
   • Look for publications, press releases, funding information
   • Determine if it's commercial or research type

3. Collect comprehensive information:

   • Basic info: name, description, mission, status
   • Entity data (see YAML_FIELDS.md for structure):
     • Commercial: founded date, headquarters, legal name, website, industry
     • Research: start date, publication date, objectives, methodology
   • Taxonomy: primary_focus, ai_technology, aging_approach, target_biology
   • Organizations: all related organizations with roles and descriptions
   • Products: platforms, methodologies, therapeutics
   • People: key researchers, founders, PIs with affiliations
   • Links: websites, publications, GitHub repos, databases
   • Technical details: AI methods, system architecture, scientific background
   • Finances: funding rounds, grants (if available)
   • Events: important milestones, publications, launches
   • Partnerships: collaborations (if any)

4. Create the case file:

   • Determine correct directory: cases/commercial/ or cases/research/
   • Generate slug from name (lowercase, hyphens, no special chars)
   • Create Markdown file with YAML frontmatter following YAML_FIELDS.md
   • Use YAML blocks (not JSON) for structured data in Markdown content
   • Save as {slug}.md in appropriate directory

5. Validate:

   • Check all required fields are present
   • Ensure YAML is valid
   • Verify structure matches YAML_FIELDS.md

6. Present to user:

   • "I've created a new case file at cases/{commercial|research}/{slug}.md"
   • "Please review and validate the information."

Workflow 2: Review and Improve Existing Case

When user chooses to review an existing case:

1. Ask user to select a case:

   • List available cases: "Here are the available cases. Which one would you like to review?"
   • Or: "Please specify the case file path or case name."

2. Read and analyze the case:

   • Read the case file
   • Compare against YAML_FIELDS.md structure
   • Check completeness of all sections
   • Identify missing fields
   • Check data quality and accuracy

3. Perform additional research:

   • Search for updated information
   • Find missing links, publications, people
   • Verify current status
   • Check for new developments

4. Create improvement plan:

   • List missing fields (high priority)
   • List outdated information (high priority)
   • List enhancements (medium/low priority)
   • Present plan to user: "I've identified the following improvements..."

5. Execute improvements:

   • Add missing fields
   • Update outdated information
   • Enhance descriptions
   • Add missing organizations, people, links
   • Improve YAML structure
   • Use YAML blocks for structured data

6. Update the case file:

   • Save improvements to the case file
   • "I've updated the case file. Please review and validate the changes."

Important Rules

1. Always start by asking what the user wants to do
2. Follow YAML_FIELDS.md structure exactly - all cases must match documented format
3. Use YAML blocks (not JSON) for structured data in Markdown content sections
4. Validate YAML - ensure all blocks are valid before saving
5. Research thoroughly - use multiple sources, verify facts
6. Be accurate - double-check dates, names, numbers
7. Save to correct location - cases/commercial/ or cases/research/ based on entity_type
8. Ask for validation - always present results for user review before finalizing

When User Starts Conversation

Your first message should be:

"What would you like to do?

• Find and document a new case
• Review and improve an existing case

Please let me know which option you prefer, and I'll guide you through the process."

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Quick Reference

• Case structure: See YAML_FIELDS.md for complete field documentation
• Project context: See memory-bank/ files for project goals and context
• Specific prompts: See prompts/ directory for detailed task-specific prompts