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ai dev tasks

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Basic Information

AI Dev Tasks is a small collection of reusable markdown prompts and a structured workflow intended to help developers and teams use AI-powered coding assistants to plan and implement software features. The repository provides a repeatable, step-by-step process that starts with creating a Product Requirement Document, generating a granular task list from that PRD, and then instructing the AI to work through individual tasks while awaiting human approval. It was created with Cursor in mind but is explicitly designed to work with other AI coding tools such as Claude Code and generic AI IDEs or CLIs. The goal is to replace large monolithic prompts with a controlled iterative approach that improves traceability, verification, and developer control when collaborating with AI on feature development.

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4497
App URL
https://github.com/snarktank/ai-dev-tasks
Github Repository
https://github.com/snarktank/ai-dev-tasks/blob/main/README.md

App Details

Features
The repo contains three primary markdown templates: create-prd.md to guide PRD creation, generate-tasks.md to break a PRD into numbered actionable tasks, and process-task-list.md to orchestrate per-task implementation and completion marking. It includes tool-specific instructions for Cursor and Claude Code, examples of how to add commands to CLAUDE.md and .claude/commands, and guidance for copying the files into a project. The workflow supports iterative review where the AI pauses for approval, tracks completed tasks visually, and offers tips for improving results such as using higher-capability models or MAX mode in Cursor. The files are lightweight and designed to be adapted to other AI assistants and repositories.
Use Cases
This project helps developers reliably use AI assistants to build features by imposing structure on the end-to-end process: define scope with a PRD, generate a detailed task plan, and implement tasks one at a time with human review. That structure reduces the risk of AI producing sprawling or incorrect changes, makes debugging and verification easier, and gives teams a clear progress signal as tasks are completed. It also provides practical integration instructions for common AI tooling, suggests best practices like tagging PRD filenames, and encourages adapting prompts to fit specific codebases. Overall it improves developer confidence and control when delegating code work to AI assistants.

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