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mad-parallel

Orchestrate wave-based parallel feature implementation across isolated git worktrees.

Updated May 7, 2026
Search Tags:#feature development#dependency analysis#orchestration#git worktrees#agent teams#parallel implementation#mad workflow
One-click install
npx skills add https://github.com/tmalcolm-0607/mad-council-claw --skill mad-parallel
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mad-parallel
Source: https://github.com/tmalcolm-0607/mad-council-claw/tree/main/.claude/skills/mad-parallel
Command: npx skills add https://github.com/tmalcolm-0607/mad-council-claw --skill mad-parallel

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually coordinating parallel feature development across multiple git worktrees requires tedious tracking of dependencies, file ownership, and merge order, often leading to merge conflicts, wasted rework, and delayed milestone delivery. This Skill automates the entire end-to-end workflow for wave-based parallel feature implementation, eliminating manual coordination overhead and reducing integration risks for teams using the MAD development framework.

Core Features & Use Cases

  • Automated Dependency & Wave Orchestration: Analyzes feature dependencies to build a directed acyclic graph (DAG), assigns features to sequential execution waves to avoid conflicts, and automatically splits waves into sub-waves when they exceed the maximum allowed parallel feature limit.
  • Isolated Worktree Setup: Creates independent git worktrees for each feature with properly linked shared project resources, so implementers can work in parallel without interfering with each other or the stability of the main branch.
  • Integrated MAD Pipeline Execution: Runs the full planning, implementation, and validation workflow per feature, with native agent team support for parallel code implementation and automatic sequential fallback for environments without agent team access.
  • Use Case: A team implementing 6 independent features for a new milestone can use this Skill to automatically split them into 2 waves of 3 features each, set up isolated worktrees for each feature, run planning for all features, dispatch parallel implementers for the first wave, validate and merge each feature, then repeat the process for the second wave, cutting total implementation time by 3x compared to sequential development.

Quick Start

Provide the path to the milestone map generated by the mad-decompose skill to automatically orchestrate parallel implementation of all mapped features across isolated git worktrees.

Frequently Asked Questions about mad-parallel

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate parallel feature development across isolated git worktrees?▼

You can orchestrate parallel feature development across isolated git worktrees by using a wave-based workflow that automatically builds a dependency DAG, sets up worktrees, dispatches agent teams for implementation, and validates each feature sequentially before progressing to the next wave.

What is the best way to prevent merge conflicts when implementing multiple features in parallel?▼

The best way to prevent merge conflicts during parallel implementation is to enforce disjoint file ownership and use a directed acyclic graph to assign features to sequential execution waves, ensuring independent features are grouped safely.

How do I automatically set up git worktrees for multiple software features?▼

You can automatically set up git worktrees for multiple features by providing a milestone map, which triggers the creation of independent worktrees with properly linked shared project resources for each mapped feature.

Can I run parallel code implementation without agent team support?▼

Yes, you can run parallel code implementation without agent team access because the workflow includes an automatic sequential fallback mechanism that executes features one by one within the same isolated worktree pipeline.

How does wave-based execution handle more parallel features than the maximum allowed limit?▼

Wave-based execution handles exceeding the maximum parallel feature limit by automatically splitting the current wave into sub-waves, ensuring the number of concurrently implemented features never surpasses the configured threshold.

Do I need a milestone map before starting parallel worktree orchestration?▼

Yes, you need a milestone map generated by the mad-decompose skill to provide the necessary feature dependencies and structure before automatically orchestrating parallel implementation across isolated git worktrees.

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