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 makenotion/notion-cookbook --skill meeting-intelligencegit clone --depth 1 https://github.com/makenotion/notion-cookbookWrote 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/makenotion/notion-cookbook/meeting-intelligence)<a href="https://agentmods.dev/skills/makenotion/notion-cookbook/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/makenotion/notion-cookbook/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/makenotion/notion-cookbook/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/makenotion/notion-cookbook/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 | $0.00050 | $0.02399 |
| Opus 5 | $0.00025 | $0.01200 |
| Sonnet 5 | $0.00010 | $0.00480 |
| Haiku 4.5 | $0.00005 | $0.00240 |
Grade A, and why
notion-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- notion-meeting-intelligence — 100% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Intelligence
Prepares you for meetings by gathering context from Notion, enriching it with Claude research, and creating comprehensive meeting materials. Generates both an internal pre-read for attendees and an external-facing agenda for the meeting itself.
Quick Start
When asked to prep for a meeting:
- Gather Notion context: Use
Notion:notion-searchto find related pages - Fetch details: Use
Notion:notion-fetchto read relevant content - Enrich with research: Use Claude's knowledge to add context, industry insights, or best practices
- Create internal pre-read: Use
Notion:notion-create-pagesfor background context document (for attendees) - Create external agenda: Use
Notion:notion-create-pagesfor meeting agenda (shared with all participants) - Link resources: Connect both docs to related projects and each other
Meeting Prep Workflow
Step 1: Understand meeting context
Collect meeting details:
- Meeting topic/title
- Attendees (internal team + external participants)
- Meeting purpose (decision, brainstorm, status update, customer demo, etc.)
- Meeting type (internal only vs. external participants)
- Related project/initiative
- Specific topics to cover
Step 2: Search for Notion context
Use Notion:notion-search to find:
- Project pages related to meeting topic
- Previous meeting notes
- Specifications or design docs
- Related tasks or issues
- Recent updates or reports
- Customer/partner information (if applicable)
Search strategies:
- Topic-based: "mobile app redesign"
- Project-scoped: search within project teamspace
- Attendee-created: filter by created_by_user_ids
- Recent updates: use created_date_range filters
Step 3: Fetch and analyze Notion content
For each relevant page:
1. Fetch with Notion:notion-fetch
2. Extract key information:
- Project status and timeline
- Recent decisions and updates
- Open questions or blockers
- Relevant metrics or data
- Action items from previous meetings
3. Note gaps in information
What ships with it
14 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.
- evaluations/decision-meeting-prep.json 3.4 KB
- evaluations/README.md 3.9 KB
- evaluations/status-meeting-prep.json 3.2 KB
- examples/customer-meeting.md 3.1 KB
- examples/executive-review.md 2.1 KB
- examples/project-decision.md 13 KB
- examples/sprint-planning.md 2.1 KB
- reference/brainstorming-template.md 1.5 KB
- reference/decision-meeting-template.md 1.8 KB
- reference/one-on-one-template.md 940 B
- reference/retrospective-template.md 941 B
- reference/sprint-planning-template.md 1.3 KB
- reference/status-update-template.md 1.3 KB
- reference/template-selection-guide.md 2.2 KB
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 · 298 lines · 50 tokens per session scan A 8abefc02d5e2
notion-meeting-intelligence is a skill published in the GitHub repository makenotion/notion-cookbook (206 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 2,399 once invoked, about $0.0003 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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