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ai-team-orchestration

Orchestrate multi-agent development teams with file-based sprint planning and QA handoffs.

Updated Mar 11, 2026
Search Tags:#sprint planning#multi-agent orchestration#GitHub Issues#project bootstrap#context recovery#agent roles#QA sign-off
One-click install
npx skills add https://github.com/selfagency/agentsy --skill ai-team-orchestration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-team-orchestration
Source: https://github.com/selfagency/agentsy/tree/main/.agents/skills/ai-team-orchestration
Command: npx skills add https://github.com/selfagency/agentsy --skill ai-team-orchestration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It solves the problem of losing momentum and context when building software with multiple AI agents by providing a repeatable, file-based workflow for planning, execution, QA, and recovery.

Core Features & Use Cases

  • Cross-chat team orchestration: Run parallel dev, QA, and DevOps streams with a human message bus and clear role boundaries (Producer plans/merges; others execute and verify).
  • Sprint planning and structured outputs: Generate sprint plans, progress trackers, and done/hand-off docs so each phase can be resumed even if a chat overflows.
  • Persistent shared memory via files: Establish a project brief as the single source of truth, plus mandatory handoff protocol to ensure context survives across agent conversations.
  • Workflow safeguards: Enforce anti-patterns (no producer coding, no rebases, no squash merges, file bugs to GitHub Issues, and keep QA from modifying code).

Quick Start

Tell your AI to bootstrap a new project with the ai-team-orchestration workflow by creating PROJECT_BRIEF.md, running a brainstorm with named agents (producer, product designer, art director, frontend, backend, DevOps, QA), generating docs/sprint-1/plan.md and docs/sprint-1/progress.md, executing sprint 1, and producing docs/sprint-1/done.md plus any required GitHub Issues.

Frequently Asked Questions about ai-team-orchestration

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

FAQPage Schema
How do I maintain shared context across multiple AI agents during sprint planning?▼

To maintain shared context across multiple AI agents during sprint planning, use a file-based workflow with structured outputs like PROJECT_BRIEF.md and sprint plan documents to serve as persistent shared memory across conversations.

What is multi-agent orchestration for software development?▼

Multi-agent orchestration for software development coordinates parallel dev, QA, and DevOps streams using a human message bus and clear role boundaries, ensuring a Producer plans while execution agents code and verify.

How do I bootstrap a new project with a multi-agent AI development team?▼

To bootstrap a multi-agent AI development team, create a PROJECT_BRIEF.md, run a brainstorm with named agents, generate docs/sprint-1/plan.md and progress.md, execute the sprint, and produce a done.md handoff document.

Can I recover project context after a chat overflow mid-delivery?▼

You can recover project context after a chat overflow mid-delivery by relying on mandatory cross-chat handoff protocols and persistent file outputs like progress trackers and done docs that survive across agent conversations.

What workflow safeguards are enforced during multi-agent orchestration?▼

Workflow safeguards enforced during multi-agent orchestration include preventing producers from coding, banning rebases and squash merges, restricting QA from modifying code, and requiring bug tracking via GitHub Issues.

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