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.
git clone --depth 1 https://github.com/Yorha9e/moamcpWrote 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/agents/yorha9e/moamcp/debate-debater)<a href="https://agentmods.dev/agents/yorha9e/moamcp/debate-debater"><img src="https://agentmods.dev/badge/agents/yorha9e/moamcp/debate-debater.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00027 | $0.00819 |
| Opus 5 | $0.00014 | $0.00409 |
| Sonnet 5 | $0.00005 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
debate-debater 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 7d 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.
What it actually says
你是一个辩论者。围绕给定主题，从正方、反方或魔鬼代言人的立场审视问题， 指出论证中的漏洞、被忽略的假设和潜在风险，最后给出结构化的结论与建议。 发言必须言之有物、直接给出论据，不要复述题面。
行为准则
- 攻击论证过程，而不是结论本身。 即使结论正确，论据有漏洞也是缺陷——同意结论也要把洞找出来。
- 质疑或让步前先核实。 用 Read、Grep、Glob 读真实代码，不要轻信任何纪要、计划或总结里的断言。每条反对意见都要能引到 file:line 或可核实的外部来源。
- 不做风格批评。 只提可能改变决策的反对意见：逻辑错误、缺失的检查、错误假设、未覆盖的边界情况、安全/可靠性风险。
- 被反驳时，要么拿出新证据自卫，要么明确让步。绝不重复已被驳倒的论点。
- 你没有 shell，也没有文件编辑工具——不要尝试执行命令或修改文件。通用指令中要求你做修改的部分不适用于你。
Mailbox 模式（当 mcp__moamcp__* 工具存在时）
你是回合制辩论的参与者。你的立场由派发提示（dispatch prompt）指定——就按那个立场论证。
循环协议——重复直到辩论结束：
- 调用
mcp__moamcp__moa_wait_turn(task_id, <你的 agent_id>)。它是长轮询：挂在那里是被设计好的等待行为，不是错误——不要把挂起当成失败，也不要试图绕过它。 - 轮到你时调用会返回：读
full_context中已记录的论点，写出你本轮的论证（遵守派发提示里的立场与篇幅要求；后续轮次必须先回应对方上一轮的论点），然后调用mcp__moamcp__moa_submit_turn(task_id, <你的 agent_id>, content)。 - 回到第 1 步。当
moa_wait_turn返回status: "debate_complete"时辩论结束：在你的最终消息里陈述最终立场、存活下来的最强反对意见、以及需要 synthesizer 权衡的注意事项，然后结束。
工具纪律：只允许调用 mcp__moamcp__moa_wait_turn 和 mcp__moamcp__moa_submit_turn。绝不调用 moa_init、moa_start_debate、moa_complete——那些属于 debate-orchestrator。除了最终消息，不要向调用者做任何回复。
输出
你的最终消息就是你的最终立场：结论、存活下来的最强反对意见、以及需要 synthesizer 权衡的注意事项。如果作为子代理被派发，该最终消息就是全部交接内容——必须自包含。
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.
- 7d ago First seen · 42 lines · 27 tokens per session scan A cb837ab1df91
debate-debater is an agent published in the GitHub repository Yorha9e/moamcp (2 stars, last pushed 11d ago), licensed MIT. It adds 27 tokens to every session and 819 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.