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OneWave-AI

Agent Team Builder skill

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

$npx skills add OneWave-AI/claude-skills --skill agent-team-builder
View this skill on GitHubOneWave-AI/claude-skills/agent-team-builder/SKILL.md
Pack stars
423
Pack installs
72k
Alyn Score
58/99
Updated
22 days ago
License
MIT
– views – installs via Alyn

OneWave-AI

Agent Team Builder

Install Agent Team Builder

Pick your agent. Every command is ready to paste.

$npx skills add OneWave-AI/claude-skills --skill agent-team-builder

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads agent-team-builder when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the agent-team-builder skill”. In Codex, type $agent-team-builder.

The skill itself

SKILL.md

FolderGitHub

Agent Team Builder

Design and generate production-ready multi-agent team configurations for business workflows through an interactive discovery session. This skill generates configuration files; it does not execute or deploy agents.

Contents

  • references/team-templates.md — Sales, Support, Research, and Content team starting points.
  • references/config-schema.md — Full team-config.yaml schema plus advanced features (A2A messaging, scaling, shared context).
  • references/output-files.md — Files to generate and the final response format.

Workflow

Always complete discovery before designing. Never generate a team config without understanding the business process first.

  1. Run discovery. Ask the user, one area at a time:

    • Process name (what to automate).
    • Current state (who is involved, handoff points).
    • Pain points (where delays, errors, or bottlenecks occur).
    • Volume (runs per day/week/month).
    • Success metrics (time, error rate, satisfaction).
    • Constraints (compliance, approval gates, human-in-the-loop).
    • Integrations (CRM, email, Slack, databases, APIs).
  2. Design the team architecture. Determine the minimum number of agents (typically 3-7). Select role types as needed:

    • Coordinator — orchestrates workflow, routes tasks, handles exceptions.
    • Specialist — deep expertise in one domain.
    • Validator — quality assurance, compliance checking, output review.
    • Interface — handles external communication.
    • Data — manages retrieval, transformation, and storage.

    Pick a communication pattern: hub-and-spoke (sequential), pipeline (linear), mesh (collaborative), or broadcast (notification). Start from a template in references/team-templates.md when one fits.

  3. Specify each agent. Define: Agent ID, Role Title, full production-ready System Prompt, Tool Access (least privilege), Input Schema, Output Schema, Handoff Rules, Escalation Rules, Success Criteria, and Failure Modes.

  4. Generate the configuration files. Produce team-config.yaml, per-agent agents/{id}/prompt.md, workflow.md, and test-scenarios.yaml per references/output-files.md, conforming to references/config-schema.md.

  5. Present the design using the response format in references/output-files.md.

Execution Rules

  1. Always start with discovery.
  2. Apply principle of least privilege — give each agent only the tools and access it needs.
  3. Design for failure — every agent gets failure modes and recovery strategies.
  4. Keep a human in the loop — include escalation paths for high-stakes decisions.
  5. Define measurable outcomes — every agent gets trackable success criteria.
  6. Start small — recommend 3-4 agents and expand based on performance data.
  7. Document everything — keep the generated config self-documenting and maintainable.
  8. Generate test scenarios so the team can be validated before deployment.
  9. Recommend a pilot phase before full deployment.
  10. Never include API keys, passwords, or secrets in generated config; use environment variable references.

The generated team-config.yaml is designed to be consumed by an agent orchestration framework. Treat all generated system prompts as starting points to refine against real-world performance.

Tips for Agent Team Builder

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
  • • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Agent Team Builder

What does the Agent Team Builder skill do?

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

How do I install Agent Team Builder?

Run "npx skills add OneWave-AI/claude-skills --skill agent-team-builder" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Agent Team Builder work in Cursor and Codex?

Yes. Agent Team Builder uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Agent Team Builder free?

Agent Team Builder is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.

Who made Agent Team Builder?

Agent Team Builder is published by OneWave-AI in the OneWave-AI/claude-skills repository, together with 29 other skills.

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