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team-lifecycle-v4

Orchestrate multi-agent software development workflows across specification, planning, implementation, testing, and review.

511|63|Updated Mar 17, 2026
Search Tags:#workflow#multi-agent#orchestration#checkpoint#session-management#code-review#team
One-click install
npx skills add https://github.com/catlog22/Maestro-Flow --skill team-lifecycle-v4
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: team-lifecycle-v4
Source: https://github.com/catlog22/Maestro-Flow/tree/main/.claude/skills/team-lifecycle-v4
Command: npx skills add https://github.com/catlog22/Maestro-Flow --skill team-lifecycle-v4

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinate and automate end-to-end software development workflows across multiple AI agents and human inputs to eliminate manual orchestration, reduce context loss between phases, and enforce quality gates and recovery procedures.

Core Features & Use Cases

  • Coordinator-driven orchestration: Text-level analysis, role registry, task dispatch, and spawn templates for background worker agents.
  • Full lifecycle pipelines: Spec, planning, implementation, testing, review, and optional supervisor checkpoints with message-bus state updates.
  • Session management & recovery: Structured session directory, supervisor resident agent, artifact-based handoffs, exploration cache, and specialist injection based on tech signals.
  • Use Case: Run a full-lifecycle session to generate product brief, requirements, architecture, epics, then plan and execute implementation tasks with automated testing and review.

Quick Start

Invoke the coordinator with a task description such as: orchestrate team lifecycle v4 to implement OAuth2 authentication with refresh tokens.

Frequently Asked Questions about team-lifecycle-v4

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

FAQPage Schema
How do I orchestrate multi-agent workflows for end-to-end software development?▼

Multi-agent orchestration automates end-to-end software delivery by coordinating specialized AI agents across specification, planning, implementation, testing, and review phases. It uses a coordinator to dispatch tasks, manage sessions, and enforce checkpoint supervision with message-bus state updates.

How does session management and recovery work in multi-agent code generation pipelines?▼

Session management maintains a structured directory and supervisor agent to track message-bus state updates and artifact handoffs. This enables workflow recovery, preserves context between phases, and allows specialist agent injection based on detected technical signals.

What is the best way to automate code review and testing across multiple AI agents?▼

Automating code review and testing across AI agents requires a plan-driven dynamic task dispatch system with checkpoint supervision. The coordinator enforces quality gates by routing artifacts through testing and review phases via message-bus state updates.

Can I use checkpoint supervision to enforce quality gates in automated software delivery?▼

Yes, checkpoint supervision enforces quality gates in automated software delivery by validating artifact handoffs between specification, implementation, and review phases. The supervisor resident agent monitors message-bus state updates to gate phase transitions.

Do I need a role registry to coordinate specification and planning tasks for AI agents?▼

Yes, a role registry is required to coordinate specification and planning tasks for AI agents. The orchestration workflow relies on the registry to define responsibilities, spawn specialist agents, and drive dynamic task dispatch across the full development lifecycle.

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