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 AbyssCN/oh-my-dag --skill omd-councilgit clone --depth 1 https://github.com/AbyssCN/oh-my-dagWrote 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/abysscn/oh-my-dag/omd-council)<a href="https://agentmods.dev/skills/abysscn/oh-my-dag/omd-council"><img src="https://agentmods.dev/badge/skills/abysscn/oh-my-dag/omd-council/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/abysscn/oh-my-dag/omd-council"><img src="https://agentmods.dev/badge/skills/abysscn/oh-my-dag/omd-council.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 | $0.00090 | $0.00970 |
| Opus 5 | $0.00045 | $0.00485 |
| Sonnet 5 | $0.00018 | $0.00194 |
| Haiku 4.5 | $0.00009 | $0.00097 |
Grade A, and why
omd-council 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 11d 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
/omd-council — 多视角议会
宽解空间(多个合理方案、拿不准)别给平均答案——调 omd MCP dag_research(可能带 mcp__omd__ 前缀;未加载先 ToolSearch "dag_research"),council: true。解空间宽时一次性答案落在概率分布的平庸中心;多样 persona 把生成拉进不同专家区,多 lens judge 抵单评判偏见。diversity > volume,不是重采样 N 遍。
用法
question= 问题 + 你整理的上下文(现状/约束/已知选项);council: true;深题加super: true(全 framing × 全评判维度)。- 返回
{runId, reportPath, summary}——summary 进对话,全文在 reportPath(.omd/research/),关键决策 Read 报告看各 lens 冠军 + 评审细节,别只看 summary。 - 转述:冠军 + 为何胜 + 从亚军嫁接了什么(不是 N 选 1 裸结论)+ 你自己的判断(你有议会没有的对话上下文)。
三个 default lens(persona conditioning)
| lens | persona | angle |
|---|---|---|
| mvp | 务实交付型工程主管 | 最小可行切口,最快验证闭环,砍非核心 |
| risk | 资深 SRE + 安全工程师 | 从失败模式/边界/不可逆点倒推,先堵风险 |
| first-principles | 第一性原理思考者 | 重构问题本质,质疑前提,找最简结构 |
接地档(领域岔口 · 反 happy-path)
领域正确性岔口(会计/法务/运营)+ 真实世界脏乱 + 选错难逆 → 默认 lens 太泛,换四步:
- 市场先验(别假设):先用
dag_research(普通检索版,不开 council)查竞品/实务真实做法,当 persona 的硬证据基线。query 要短(长 query 检索零结果 → 拆焦点词)。 - 领域角色 persona:换题目真实角色——每天干这活的操作者 / 合规审计 / 自动化第一性 / 生命周期末端(如年底关账)。各角色独立并行判、不互看,都喂步骤 1 的硬证据(从事实吵不从 vibe)。
- 判据轴 = 反 happy-path 场景:明令用脏数据 · 并发 · 部分失败 · 跨边界 · 生命周期末端 · 量级膨胀去判每个选项(「auto-X 在年底会不会滚成噩梦」),否则 persona 也按 happy-path 答。
- judge 择优 + 嫁接:看共识(全票同向 = 强信号);冠军 + 嫁接正交亮点(最优解常是让争论变小,非选一项)。领域红线不下放(council 作输入,owner 终裁);收敛 owner 直觉的对内核,不硬否。
与既有 skill 的边界
- omd-council = 宽解空间横向铺宽多方案择优。纵向掘深单条决策线 → /omd-grill(岔口可 fire council);审已写代码 → /omd-review;根因调试 → /omd-debug。
- 单一明确解直接做;定型结论走 /omd-note 或 map_rule(/omd-rule)落盘。
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.
- 11d ago First seen · 36 lines · 90 tokens per session scan A f22005ca21ac
omd-council is a skill published in the GitHub repository AbyssCN/oh-my-dag (39 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 970 once invoked, about $0.0005 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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