Parallel Subagent Developer

About

This skill optimizes the AI development workflow by executing complex, decomposed plans through parallelized subagents. By splitting independent tasks into batches of two, it significantly reduces total implementation time while maintaining high code quality through mandatory review checkpoints between every batch. It utilizes a manifest-driven architecture to provide fresh context for every task, preventing context pollution and ensuring strict adherence to Test-Driven Development (TDD) principles and project-specific best practices.

Key Features

• Automated feedback loops for fixing review-identified issues
• Manifest-based task tracking and status management via JSON
• 0 GitHub stars
• Isolated subagent contexts to prevent cross-task context pollution
• Parallel execution of up to two subagents for independent tasks
• Mandatory code review quality gates between implementation batches

Use Cases

• Scaling development speed on large features with independent sub-modules
• Implementing complex plans where task isolation is critical for code quality
• Automating the TDD lifecycle across multiple concurrent workstreams