skills/ai-team-orchestration/SKILL.md
Bootstrap and run a multi-agent AI development team. Use when: starting a new software project with AI agents, setting up parallel dev/QA teams, creating sprint plans, writing brainstorm prompts with distinct agent voices, recovering a project workflow, or planning sprints.
npx skillsauth add williamlimasilva/.copilot ai-team-orchestrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Agent | Name | Role | Focus | |-------|------|------|-------| | Producer | Remy | Sprint planning, coordination, merging PRs | Scope control, handoffs, issue triage | | Product Designer | Kira | UX, mechanics, user experience | Fun factor, user flows, feature design | | Visual/Art Director | Milo | CSS, animations, visual identity | Design system, polish, accessibility | | Frontend Engineer | Nova | UI framework, state management, components | React/Vue/Svelte, client-side logic | | Backend Engineer | Sage | API, database, auth, security | Server-side logic, infrastructure | | DevOps Engineer | Dash | CI/CD, cloud deployment, pipelines | GitHub Actions, Azure/AWS/GCP | | QA Engineer | Ivy | E2E tests, automation, playtesting | Playwright/Cypress, bug filing, sign-off |
Customize names and roles for your project. Not every project needs all roles.
The human (CEO) is the message bus between parallel chats:
┌────────────────────────────────────────┐
│ @ai-team-producer — Plans, merges │
│ NEVER writes code │
└────────────────┬───────────────────────┘
│ Human carries messages
┌──────────┼──────────┐
▼ ▼ ▼
┌──────────┐ ┌────────┐ ┌────────┐
│@ai-team │ │@ai-team│ │DevOps │
│-dev │ │-qa │ │(on │
│ │ │ │ │demand) │
│ Nova │ │ Ivy │ │ │
│ Sage │ │ │ │ │
│ Milo │ │ │ │ │
│ │ │feature/│ │feature/│
│ feature/ │ │qa-N │ │devops-N│
│ sprint-N │ └────────┘ └────────┘
└──────────┘
Each team works in a separate VS Code window with its own clone:
git clone <repo> project-dev # Dev team
git clone <repo> project-qa # QA
git clone <repo> project-devops # DevOps (only when needed)
The single source of truth across all chats. See the project brief template.
Required sections (do not abbreviate):
See the brainstorm format. Key: name each agent explicitly with distinct personality and perspective. Require at least 2 genuine disagreements to prevent groupthink.
See the sprint plan template. Every sprint gets:
docs/sprint-N/plan.md — prioritized tasks, success criteriadocs/sprint-N/progress.md — live tracker, enables recoverydocs/sprint-N/done.md — handoff doc written at sprint endRead PROJECT_BRIEF.md, then read docs/sprint-N/plan.md. Execute Sprint N.
First: git pull origin main && git checkout -b feature/sprint-N
Close GitHub Issues in commits: "fix: description (Fixes #NN)"
Update docs/sprint-N/progress.md after each phase.
When done, push and create PR: git push origin feature/sprint-N
Follow Sections 12-14 of PROJECT_BRIEF.md.
After dev merges, QA does a full playthrough:
Read PROJECT_BRIEF.md. You are Ivy (QA).
Sprint N is merged to main. Do full playthrough.
File bugs as GitHub Issues. Write docs/qa/sprint-N-signoff.md.
When a chat gets long (>100 messages), save state and start fresh:
Before closing:
docs/sprint-N/progress.md with current statusPROJECT_BRIEF.md sections 7+8docs/sprint-N/done.mdCold start prompt:
Read PROJECT_BRIEF.md and docs/sprint-N/progress.md.
Continue from where it left off.
See anti-patterns reference for the full list. Top 5:
| Don't | Do Instead | |-------|------------| | Rebase feature branches | Merge (rebase loses commits) | | Producer writes code | Producer only plans, merges, files issues | | Batch "fix everything" commits | One commit per fix with issue reference | | Vague brainstorm prompts | Name each agent with distinct perspective | | Keep bugs only in chat | File GitHub Issues (chat context dies) |
development
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
development
Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.
devops
Query AWS resources using natural language. Covers EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, and more. Strictly read-only — no writes, deletes, or mutations.
devops
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.