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 guoliang1114-boop/AriaAI --skill meeting-intelligencegit clone --depth 1 https://github.com/guoliang1114-boop/AriaAIWrote 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/guoliang1114-boop/ariaai/meeting-intelligence)<a href="https://agentmods.dev/skills/guoliang1114-boop/ariaai/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/guoliang1114-boop/ariaai/meeting-intelligence/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/guoliang1114-boop/ariaai/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/guoliang1114-boop/ariaai/meeting-intelligence.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 | $0.00087 | $0.02386 |
| Opus 5 | $0.00044 | $0.01193 |
| Sonnet 5 | $0.00017 | $0.00477 |
| Haiku 4.5 | $0.00009 | $0.00239 |
Grade A, and why
meeting-intelligence 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 4d 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Intelligence
Extract structured meeting intelligence from raw transcripts, interview notes, or workshop outputs. Turns unstructured conversation into actionable meeting minutes.
When To Use
- 用户粘贴了会议录音转写文本
- 用户提供了访谈笔记
- 用户要求整理会议纪要
- 用户要求提取会议中的决策/待办/风险
- 用户要求总结工作坊产出
Tools
| Tool | Purpose |
|---|---|
update_project_markdown_document |
Save meeting minutes as project document |
write_project_office_document |
Generate meeting minutes as Word/PDF |
Diagnostic Intake
Before extraction, determine only the facts that materially affect the result:
- Is the input a transcript, rough notes, interview record, or workshop output?
- Is the meeting date known? It is required to convert relative dates such as “next Wednesday”.
- Are speaker names reliable, role-only, or absent?
- Does the user want Quick, Standard, or Deep output, and should it be saved?
- Which project/client scope applies, and is any content too sensitive for a broad summary?
Do not block useful analysis when metadata is missing. Mark unknown participants, owners, deadlines, and decision status as “待确认”; preserve the original relative date when it cannot be converted. Ask a short follow-up only when the ambiguity would change attribution, commitment, confidentiality, or the requested artifact.
Treat contradictions as evidence to surface, not text to reconcile silently. When two speakers disagree, record both positions and the unresolved decision owner.
Workflow
1. Receive → Get transcript/notes from user
2. Parse → Identify speakers, topics, timestamps
3. Extract → Pull out decisions, actions, risks, questions
4. Structure → Organize into standard meeting minutes format
5. Save → Persist to project space
Output Format
Always produce meeting minutes in this structure:
# 会议纪要：[会议主题]
**日期**：YYYY-MM-DD
**参会人**：[名单]
**时长**：[时长]
---
## 📋 议题摘要
| # | 议题 | 讨论要点 | 结论 |
|---|------|---------|------|
| 1 | ... | ... | ... |
---
## ✅ 关键决策
| # | 决策内容 | 决策人 | 影响范围 |
|---|---------|--------|---------|
| 1 | ... | ... | ... |
---
## 📌 行动项（Action Items）
| # | 待办事项 | 负责人 | 截止日期 | 优先级 |
|---|---------|--------|---------|--------|
| 1 | ... | ... | ... | 高/中/低 |
---
## ⚠️ 风险与问题
| # | 风险/问题 | 影响 | 建议处理方式 |
|---|----------|------|-------------|
| 1 | ... | ... | ... |
---
## ❓ 待解决问题
- [ ] 问题 1（需要谁回复）
- [ ] 问题 2（需要谁回复）
---
## 💡 关键洞察
- 洞察 1
- 洞察 2
---
## 📅 下次会议
- **时间**：[建议时间]
- **议题**：[待讨论事项]
- **准备**：[需要提前准备的材料]
What ships with it
2 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.
- 4d ago Changed · +22 lines 0f74274d47ec
- 12d ago First seen · 255 lines · 87 tokens per session scan A c35376d0269b
meeting-intelligence is a skill published in the GitHub repository guoliang1114-boop/AriaAI (37 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 2,386 once invoked, about $0.0004 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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