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-to-agentgit 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-to-agent)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/agent-to-agent"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-to-agent/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-to-agent"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-to-agent.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 | $0.00047 | $0.00882 |
| Opus 5 | $0.00023 | $0.00441 |
| Sonnet 5 | $0.00009 | $0.00176 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
agent-to-agent 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-to-Agent (A2A) Communication Protocol
Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file .a2a-context.json and the Agent tool.
Contents
references/protocol.md— message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management.references/registry.md— agent registration, capability discovery, built-in agent templates.references/patterns.md— request/response, pipeline, fan-out/fan-in, conversation, supervisor.references/handoff.md— structured handoff, acceptance, rejection, chain tracking.references/error-handling.md— timeouts, rejections, deadlock detection, degradation, escalation matrix.references/workflows.md— worked examples (research+writer, code+review, sales+technical).references/operations.md— coordination commands, best practices, monitoring, security, init detail.
Workflow
- Understand the goal. Determine what the user wants to accomplish with multiple agents.
- Design the team. Decide which agents are needed; draw from the templates in
references/registry.mdor write custom specs. - Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from
references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent. - Initialize. Locate the project root. Read
.a2a-context.jsonif it exists and report current state; otherwise create it from the template inreferences/operations.md. Register every agent into theagentssection perreferences/registry.md. - Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see
references/operations.md). For parallelism, issue multiple Agent tool calls in a single response. - Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task
chain. Followreferences/handoff.md. - Monitor and recover. Read
.a2a-context.jsonto track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix inreferences/error-handling.md. Cap retries at 3 before escalating to the user. - Deliver. Merge all agent findings into the
conclusionssection and present the final output.
What ships with it
7 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 · 55 lines · 47 tokens per session scan A 27ec1c2e4e73
agent-to-agent is a skill published in the GitHub repository OneWave-AI/claude-skills (288 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 882 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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