About agent-team-builder
agent-team-builder is a skill for designing multi-agent configurations for business workflows through a discovery process. It generates configuration and test files, but does not run or deploy agents.
Key features
- 01Interactive business-process discovery
- 02Agent roles and communication patterns
- 03Least-privilege tool access
- 04Failure and escalation rules
- 05YAML configs, prompts, and tests
Use cases
- 01Designing an agent team for a business process
- 02Defining agent handoffs and escalation paths
- 03Preparing configurations for framework consumption
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ onewave-ai/ claude-skills --skill agent-team-builderAfter installing, type/agent-team-builderin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/config-schema.mdoutput-files.mdteam-templates.mdDetails
From the project READMEAgent 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— Fullteam-config.yamlschema 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.
-
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).
-
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.mdwhen one fits. -
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.
-
Generate the configuration files. Produce
team-config.yaml, per-agentagents/{id}/prompt.md,workflow.md, andtest-scenarios.yamlperreferences/output-files.md, conforming toreferences/config-schema.md. -
Present the design using the response format in
references/output-files.md.
Execution Rules
- Always start with discovery.
- Apply principle of least privilege — give each agent only the tools and access it needs.
- Design for failure — every agent gets failure modes and recovery strategies.
- Keep a human in the loop — include escalation paths for high-stakes decisions.
- Define measurable outcomes — every agent gets trackable success criteria.
- Start small — recommend 3-4 agents and expand based on performance data.
- Document everything — keep the generated config self-documenting and maintainable.
- Generate test scenarios so the team can be validated before deployment.
- Recommend a pilot phase before full deployment.
- 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.