How do I start a new work session using this skill?

You can start by asking Claude to check the active initiatives folder to find the most recent session summary and current completion criteria.

Can this skill help with team collaboration?

Yes. Because the tracking system is file-based, you can commit it to Git, allowing both human team members and AI agents to share the same project context.

Does this replace external tools like Jira or Linear?

It is designed to complement them by providing deep, technical, session-level context that is easily accessible to an AI agent directly within the codebase.

Where is the project tracking data stored?

All tracking data, including initiatives and session logs, is stored locally in your project repository under the docs/workspace/ directory.

What is a session summary in this skill?

A session summary is a document created at the end of a work period that captures specific actions taken, decisions made, and remaining tasks to help the AI resume work later.

Project Tracking

Name: Project Tracking
Author: hhopkins95

byhhopkins95

Productivity & Workflow

Maintains project continuity and task organization across multiple AI coding sessions through a structured documentation workspace.

About

Project Tracking provides a standardized framework for managing long-term initiatives, session-specific progress, and ad-hoc ideas within your codebase. By organizing work into initiatives with defined lifecycles and capturing session summaries, this skill ensures that context is never lost between AI interactions. It is particularly useful for complex projects where multiple focused work periods are required, allowing the AI to seamlessly resume tasks by reviewing the structured documentation in the workspace directory.

Key Features

Session summary generation for seamless knowledge transfer
Multi-session initiative lifecycle management (Active, Backlog, Completed)
0 GitHub stars
Lightweight capture for ideas and ad-hoc todos
Standardized workspace structure in docs/workspace/
Context-rich navigation for AI agent onboarding

Use Cases

Maintaining progress on complex feature developments across several days.
Capturing spontaneous ideas and bugs without interrupting the current workflow.
Onboarding an AI agent to a project by providing a clear history of decisions and states.

About

Key Features

Session summary generation for seamless knowledge transfer
Multi-session initiative lifecycle management (Active, Backlog, Completed)
0 GitHub stars
Lightweight capture for ideas and ad-hoc todos
Standardized workspace structure in docs/workspace/
Context-rich navigation for AI agent onboarding

Use Cases

Maintaining progress on complex feature developments across several days.
Capturing spontaneous ideas and bugs without interrupting the current workflow.
Onboarding an AI agent to a project by providing a clear history of decisions and states.