Introduction

This is TrackBuddy, a Slack-integrated work logging and progress summary. The project leverages AWS services including Bedrock, Lambda, and DynamoDB to provide intelligent work tracking capabilities directly within Slack.

Available Slack Commands:

• /log [work description] - Log daily work activities
• /1:1 - Generate 1:1 meeting summaries (past 7 days)
• /midpoint - Generate mid-year performance summaries (past 6 months)
• /quarterly [Q1/Q2/Q3/Q4] - Generate quarterly summaries (auto-detects current quarter if not specified)

Architecture Deployed:

• Slack Integration: Slack Bolt framework handling slash commands
• Bedrock Agent: Claude 3.5 Haiku model with action groups
• Lambda Functions: SlackHandlerFunction and ActionFunction
• DynamoDB: Work logs storage with user_id and timestamp indexing
• API Gateway: REST API for Slack webhook integration
• IAM Roles: Proper permissions for all AWS services

📋 For detailed architecture information, see design.md

Use case

TrackBuddy solves the common workplace challenge of tracking daily work activities and preparing meaningful progress updates for manager meetings. Many employees struggle to remember what they accomplished throughout the week when it comes time for their 1:1 meetings with managers. This application allows users to quickly log their work activities in natural language using a simple Slack command (/log), and then automatically generates professional, comprehensive summaries using AI when they need to prepare for meetings.

Value proposition

TrackBuddy provides significant value by:

• Reducing cognitive load: No need to remember what you worked on days ago
• Improving meeting quality: Generate structured, professional summaries for manager discussions
• Increasing productivity: Quick logging with natural language - no complex forms or interfaces
• Enhancing career development: Better documentation of accomplishments and progress
• Seamless integration: Works directly in Slack where teams already communicate
• AI-powered insights: Leverages Amazon Bedrock to create meaningful summaries from raw work logs
• Multiple timeframes: Support for weekly (1:1), quarterly, and mid-year summaries

Development approach

This project was developed using AWS CDK with TypeScript for infrastructure as code, and Python for Lambda functions. The system uses a git repository for version control and follows best practices for AWS serverless architecture.

Getting Started

🚀 Ready to deploy TrackBuddy? Follow the step-by-step guide in deploy.md

Project Documentation

📚 Complete documentation available:

• deploy.md - Complete deployment guide with step-by-step instructions
• design.md - System architecture and technical design details

Thank You and Credits

• Thank you to Barry Conway and Dean Colcott for the aws-samples repository amazon-bedrock-knowledgebase-slackbot, this repository takes elements from that repository.