AI Team Orchestration Agent Skill
Bootstrap and run a lightweight multi-agent development team. Use when starting or adopting a project, planning work, coordinating implementation and optional QA, brainstorming with distinct perspectives, or preserving context across sessions.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/github/awesome-copilot --skill ai-team-orchestrationWhat comes with it
7 903 bytes besides the instructionThe instruction itself
11 sections, as written by the authorAI Team Orchestration
Use three stable agents:
| Agent | Purpose |
|---|---|
| @ai-team-producer | Clarify scope, plan proportionately, coordinate, and merge |
| @ai-team-dev | Implement, test, self-review, and prepare the pull request |
| @ai-team-qa | Independently test behavior when dedicated QA is useful |
Nova, Sage, and Milo are perspectives inside the Dev agent, not mandatory project layers.
Default Workflow
Plan -> Implement -> Test -> optional review or QA -> Merge -> update project state
Keep the workflow proportional:
- Skip formal planning for small, obvious changes.
- Use a short plan for multi-step or cross-cutting work.
- Add independent review or QA when risk, uncertainty, or repository policy justifies it.
- Let branch protection, required checks, permissions, and merge queues enforce repository merge policy.
Start or Adopt a Project
- Read existing repository instructions and documentation.
- Discover the actual stack, architecture, commands, deployment model, and risks.
- Create or update
PROJECT_BRIEF.mdonly when durable cross-session context is useful. Start from the project brief template and omit irrelevant sections. - For substantial work, create a concise plan from the sprint plan template.
- Use a separate branch or clone when parallel sessions could conflict, following the repository's own Git policy.
Execute
Producer
- Define the outcome, constraints, acceptance criteria, and explicit exclusions.
- Choose review and QA based on risk rather than ceremony.
- Keep durable project state concise and current.
Dev
- Follow repository conventions and implement the smallest complete solution.
- Run relevant checks and inspect the final diff.
- Open or update the pull request with summary, verification, and limitations.
QA
- Use only when dedicated behavioral verification adds value.
- Test the requested change and important regressions.
- Report reproducible findings and verify fixes.
Brainstorms
Use the brainstorm format for product or architecture decisions that benefit from competing perspectives. For ordinary implementation choices, let Dev decide using repository conventions.
Context Recovery
Before ending a long or interrupted session:
- Update the active plan or progress note if one exists.
- Record material decisions, blockers, and the next action in repository context.
- Use a cold-start prompt such as:
Read the repository instructions, then read whichever sources exist for this
work: the active issue or request, PROJECT_BRIEF.md, and the active plan or
progress note.
Continue from the recorded next action.
Tool and Model Inheritance
The bundled agents intentionally omit tools and model frontmatter:
- available built-in, MCP, and extension tools remain usable;
- developers keep control of model selection;
- role boundaries are defined by instructions and normal trust, permission, authentication, and approval controls.
If the environment exposes too many tools, deselect irrelevant tools or MCP servers, or use VS Code virtual-tool management. Do not add a machine-specific plugin allowlist.
Principles
- Prefer working software and clear handoffs over process artifacts.
- Follow repository policy instead of embedding universal Git commands.
- Preserve unknown work and ask before destructive or privileged actions.
- Keep bugs and important decisions in durable project systems, not only chat.
- See anti-patterns for concise lessons.
Other skills for the same job
different authors, same section of the catalogue
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
Prepares and structurally reviews readiness evidence for ISO management-system and laboratory-competence standards - ISO 13485 medical device QMS, ISO 14971 device risk management, ISO/IEC 17025 testing and calibration laboratories, and ISO 15189 medical laboratories. Use when organizing declared scope, controlled documents, risk-management files, scope of accreditation, traceability, CAPA, external-provider controls, or bounded local evidence manifests, and when separating ISO certification from laboratory accreditation, FDA QMSR inspection, CLIA certification, MDSAP, and EU MDR/IVDR evidence boundaries. Not for legal applicability, compliance, certification, or accreditation decisions; contains no clause text.
Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
Universal QA checklist for generated scientific plots: overlapping labels, clipped text, missing axes/legends, overcrowded data, and cross-journal resolution/format guidance.
Senior Elite Software Engineer (15+) and Senior Product Designer. Full workflow with planning, architecture, TDD, clean code, and pixel-perfect UX validation.
Run PinchBench benchmarks to evaluate OpenClaw agent performance across real-world tasks. Use when testing model capabilities, comparing models, submitting benchmark results to the leaderboard, or checking how well your OpenClaw setup handles calendar, email, research, coding, and multi-step workflows.
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
Verify PowerToys behavior end-to-end with the winapp CLI across two scenarios: (A) a module's release checklist against the installed build; (B) PR validation — derive each PR's checklist from its description + diff, then drive it against the installed build (a merged/shipped PR, or a whole release/hotfix set) or by building + sideloading the module when the PR isn't in the build yet (unmerged or not-yet-released). Drive each item via UIA invoke / Named Events / settings.json edits / clipboard / GPO / SendInput, and emit a structured PASS / FAIL / BLOCKED verdict per item with evidence (FAIL distinguishes product defects from stale/ambiguous checklist items). Use when asked to verify a module checklist, validate a PR, sign off a release/hotfix's PRs, or QA installed/sideloaded PowerToys bits. Combines generic winapp ui mechanics (references/winapp-ui-testing.md) with PT-specific recipes, per-scenario playbooks (references/scenarios/), and the helper .ps1 files shipped with this skill.
How to use it
Take github/ai-team-orchestration from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.