Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OneWave-AI/claude-skills --skill agent-team-buildergit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/onewave-ai/claude-skills/agent-team-builder)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/agent-team-builder"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-team-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/agent-team-builder"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-team-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00738 |
| Opus 5 | $0.00022 | $0.00369 |
| Sonnet 5 | $0.00009 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
agent-team-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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— 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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 60 lines · 44 tokens per session scan A 8d0a52ce6230
agent-team-builder is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 738 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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