How does thread isolation improve AI development?

By scoping work to a single conversation, it prevents context carryover and the performance degradation that often occurs in long, cluttered chat histories.

What is reasoning economy in PRD Planner?

Reasoning economy is the process of matching task complexity to the most efficient AI model level (e.g., using Haiku for procedural tasks and Opus for complex architecture) to optimize performance and token usage.

Does it integrate with specific project management tools?

It generates Markdown-based PRDs that are stored directly in your repository, making them easy to track via Git or copy into tools like Notion, GitHub Issues, and Jira.

What triggers the PRD Planner skill?

It activates on natural language phrases such as 'plan this feature', 'create a PRD', 'break down this task', or 'implementation plan'.

Can I use this for existing codebases?

Yes, the planner is designed to analyze current file structures and provide specific file path references to ensure the AI knows exactly where to apply changes.

PRD Planner

Name: PRD Planner
Author: jmichaelschmidt

byjmichaelschmidt

•

Productivity & Workflow

Generates structured Product Requirements Documents optimized for AI-assisted development and efficient token usage.

About

PRD Planner streamlines the transition from concept to code by breaking complex features into discrete, self-contained development tasks. It optimizes AI pair programming by assigning specific reasoning levels to each task—matching the right model capability to the task's complexity—and organizing work into isolated conversation threads. This ensures developers and AI agents maintain clear context, minimize token waste, and track project progress through structured checklists and completion logs.

Key Features

2 GitHub stars
AI-Optimized Threading: Breaks complex features into single-conversation tasks to minimize context drift.
Structured Templates: Provides a standardized format for objectives, architecture decisions, and validation targets.
Self-Contained Context: Ensures every thread contains all necessary file references and instructions for independent execution.
Reasoning Level Estimates: Matches task complexity to specific AI models for cost and performance efficiency.
Progressive Tracking: Includes built-in checklists and completion logs for transparent project management.

Use Cases

Breaking down a large feature request into a step-by-step implementation roadmap for an AI agent.
Optimizing development costs by identifying which tasks require high-reasoning models versus lightweight models.
Creating documentation for complex refactoring projects that require multiple isolated coding sessions.

About

Key Features

2 GitHub stars
AI-Optimized Threading: Breaks complex features into single-conversation tasks to minimize context drift.
Structured Templates: Provides a standardized format for objectives, architecture decisions, and validation targets.
Self-Contained Context: Ensures every thread contains all necessary file references and instructions for independent execution.
Reasoning Level Estimates: Matches task complexity to specific AI models for cost and performance efficiency.
Progressive Tracking: Includes built-in checklists and completion logs for transparent project management.

Use Cases

Breaking down a large feature request into a step-by-step implementation roadmap for an AI agent.
Optimizing development costs by identifying which tasks require high-reasoning models versus lightweight models.
Creating documentation for complex refactoring projects that require multiple isolated coding sessions.