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 tommy-ca/notion-skills --skill meeting-intelligencegit clone --depth 1 https://github.com/tommy-ca/notion-skillsWrote 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/tommy-ca/notion-skills/meeting-intelligence)<a href="https://agentmods.dev/skills/tommy-ca/notion-skills/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/tommy-ca/notion-skills/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/tommy-ca/notion-skills/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/tommy-ca/notion-skills/meeting-intelligence.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 | $0.00014 | $0.00666 |
| Opus 5 | $0.00007 | $0.00333 |
| Sonnet 5 | $0.00003 | $0.00133 |
| Haiku 4.5 | $0.00001 | $0.00067 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The Meeting Intelligence skill prepares you for productive meetings by automatically gathering relevant context, analyzing past interactions, and creating comprehensive meeting agendas. It helps ensure you enter meetings informed and prepared.
When to Use
Use this skill when you need to:
- Prepare for important meetings
- Gather context about attendees and topics
- Create comprehensive meeting agendas
- Review past interactions with participants
- Identify potential discussion points and blockers
- Prepare background materials for meetings
Features
- Context Gathering: Automatically collects relevant documentation and past interactions
- Attendee Analysis: Gathers information about meeting participants
- Agenda Creation: Generates structured meeting agendas with timing
- Background Materials: Compiles reference materials and context documents
- Risk/Blocker Identification: Surfaces potential issues to address
- Decision Tracking: Monitors previously made decisions relevant to the meeting
Requirements
- Notion API Access: For retrieving documentation and meeting history
- Calendar Integration (Optional): To pull meeting details
- Context Database: Notion database with relevant background information
- Team Database: Directory of team members and their expertise areas
Implementation Details
This skill leverages Notion as a knowledge base to:
- Search relevant documentation and past meetings
- Analyze attendee profiles and expertise areas
- Identify agenda items based on historical context
- Create structured agenda pages in Notion
- Compile background materials and references
- Track follow-up items from previous meetings
Meeting Preparation Workflow
Meeting Request
↓
Extract Meeting Details
↓
Search Relevant Context
↓
Analyze Attendees
↓
Identify Agenda Items
↓
Compile Background Materials
↓
Create Comprehensive Agenda
↓
Output: Prepared Meeting Document
Example Use Cases
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.
- 11d ago First seen · 103 lines · 14 tokens per session scan A b525db7ff62f
meeting-intelligence is a skill published in the GitHub repository tommy-ca/notion-skills (62 stars, last pushed 10mo ago), licensed MIT. It adds 14 tokens to every session and 666 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 2026-08-30.
Other skills, from other repositories
ara-research-manager
Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during…
loop-engineering
Use when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.
Wikimate Lint
A read-only checker for an organised notes vault, such as an Obsidian collection, and its Notion index. It looks for duplicates, broken links, isolated notes, and missing or invalid metadata.
harness-engineering
Use when an agent workflow needs production-like runtime controls for context, tools, permissions, observability, scheduling, evaluation, recovery, or maintenance.
agent-teams-command
Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.
agentic-engineering
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.