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 medalsoftchina/workcopilot --skill doa-mettinggit clone --depth 1 https://github.com/medalsoftchina/workcopilotWrote 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/medalsoftchina/workcopilot/doa-metting)<a href="https://agentmods.dev/skills/medalsoftchina/workcopilot/doa-metting"><img src="https://agentmods.dev/badge/skills/medalsoftchina/workcopilot/doa-metting/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/medalsoftchina/workcopilot/doa-metting"><img src="https://agentmods.dev/badge/skills/medalsoftchina/workcopilot/doa-metting.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00165 | $0.01502 |
| Opus 5 | $0.00082 | $0.00751 |
| Sonnet 5 | $0.00033 | $0.00300 |
| Haiku 4.5 | $0.00016 | $0.00150 |
Grade A, and why
doa-metting 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 9d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
会议纪要 PDF 生成
工作流
用户提供会议内容 → AI 结构化整理 → 生成 Python 脚本 → 运行输出 PDF
Step 1: 收集与整理会议内容
从用户输入中提取并结构化为以下标准章节：
必需章节
| 章节 | 内容要求 |
|---|---|
| 基本信息 | 会议主题、参会方、核心目标、日期 |
| 会议核心内容 | 按议题分节，每节含标题 + 要点 |
| 达成共识 | 编号列表，明确已确认事项 |
| 待确认项 | 编号列表，标注风险/未决事项 |
| 后续行动计划 | 表格：责任方 + 行动项 + 时间节点 |
| 会议结论 | 一句话概括结论 |
可选章节
- 附录：流程图、系统交互步骤、补充说明
- 参考资料：相关文档链接
内容整理原则
- 去除口语化表达，用专业书面语
- 每个议题提炼 3-5 个核心要点
- Action Items 必须有明确负责人和时间节点
- 待确认项标注影响范围和紧急程度
Step 2: 生成 PDF 脚本
读取 references/pdf-template.py 获取完整的生成模板。
基于模板生成定制化 Python 脚本，替换其中的内容数据。
设计规范
颜色体系（商务蓝主题）
| 角色 | 色值 | 用途 |
|---|---|---|
| PRIMARY | #1A365D |
主标题、表头、页眉线 |
| SECONDARY | #2B6CB0 |
章节标题 |
| ACCENT | #3182CE |
强调、流程编号 |
| SUCCESS | #276749 |
达成共识（✅） |
| WARNING | #C05621 |
待确认项（⚠️） |
| RED | #C53030 |
紧急/重要时间节点 |
| LIGHT_BG | #EBF4FF |
信息卡片背景 |
| TEXT | #2D3748 |
正文 |
| SUBTEXT | #718096 |
辅助文字 |
字体层级
| 元素 | 字体 | 字号 | 行距 |
|---|---|---|---|
| 大标题 | ChineseFontBold | 20pt | 28pt |
| 章节标题 | SectionHeader 组件 | 12pt | — |
| 小节标题 | ChineseFontBold | 12pt | 18pt |
| 正文 | ChineseFont | 10pt | 16pt |
| 列表项 | ChineseFont | 10pt | 16pt |
| 页眉/页脚 | ChineseFont | 7.5-8pt | — |
核心组件
模板提供以下 Flowable 组件，直接复用：
SectionHeader(text, color)— 带色块的章节标题栏（白字 + 圆角色底）InfoBox(data)— 信息卡片（浅蓝底 + 边框，用于基本信息展示）DividerLine()— 分隔线- 页眉页脚 —
header_footer()回调，含蓝线 + 项目名 + 页码
表格样式要点
- 表头：PRIMARY 背景 + 白色文字
- 首列：LIGHT_BG 背景 + PRIMARY 粗体文字
- 交替行色：
LIGHT_GRAY (#F7FAFC) - 边框：
BORDER (#CBD5E0)0.4-0.5pt - 内边距：3mm 上下 + 3mm 左右
内容与样式映射
| 内容类型 | 实现方式 |
|---|---|
| 基本信息（主题/参会方/目标） | InfoBox 组件 |
| 章节标题（一、二、三…） | SectionHeader 组件，不同章节用不同颜色 |
| 议题小标题 | h2 样式（SECONDARY 粗体） |
| 要点列表 | bullet 样式（• 前缀，12mm 缩进） |
| 达成共识 | consensus 样式（✅ 前缀，绿色） |
| 待确认项 | warning 样式（⚠️ 前缀，橙色） |
| 行动计划 | Table（3列：责任方/行动项/时间） |
| 结论高亮 | Table 单行（LIGHT_BG + PRIMARY 边框） |
| 流程步骤 | Table（3列：序号/角色/操作） |
What ships with it
1 file 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.
- 9d ago First seen · 140 lines · 165 tokens per session scan A edbff5760dbf
doa-metting is a skill published in the GitHub repository medalsoftchina/workcopilot (4 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 165 tokens to every session and 1,502 once invoked, about $0.0008 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.
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