agent-team-builder: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

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.

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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.

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.

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.md when 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-agent agents/{id}/prompt.md, workflow.md, and test-scenarios.yaml per references/output-files.md, conforming to references/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.

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File metadata

name: agent-team-builder
description: 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.
tools: Read, Write, Bash, Glob
model: inherit

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---
name: agent-team-builder
description: 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.
tools: Read, Write, Bash, Glob
model: inherit
---

# Agent Team Builder

## 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.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 283 stars, 43 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: OneWave-AI/claude-skills
License: MIT
Version: 1.0.0
Last GitHub push: Aug 11, 2026
Registry updated: Sep 3, 2026
Instruction path: agent-team-builder/SKILL.md @ 82859c0ebaff

Version reported in registry metadata; check source releases before relying on it.

Quality

68/100

Promising

Trust

62/100

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More details

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Source: OneWave-AI/claude-skills
Indexed by: OpenAgentSkill community index

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