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 panaversity/agentfactory-business-plugins --skill meeting-intelligencegit clone --depth 1 https://github.com/panaversity/agentfactory-business-pluginsWrote 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/panaversity/agentfactory-business-plugins/meeting-intelligence)<a href="https://agentmods.dev/skills/panaversity/agentfactory-business-plugins/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/panaversity/agentfactory-business-plugins/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/panaversity/agentfactory-business-plugins/meeting-intelligence"><img src="https://agentmods.dev/badge/skills/panaversity/agentfactory-business-plugins/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 | $0.00084 | $0.01180 |
| Opus 5 | $0.00042 | $0.00590 |
| Sonnet 5 | $0.00017 | $0.00236 |
| Haiku 4.5 | $0.00008 | $0.00118 |
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 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MEETING INTELLIGENCE WORKFLOW
STEP 1 — LOAD CONTEXT
Read work.local.md in the current working directory.
If it does not exist, tell the user to run /agentic-office:setup first.
STEP 2 — IDENTIFY PHASE
The Three-Phase Model
BEFORE (30 minutes pre-meeting): Purpose: Ensure every attendee (especially you) is prepared to contribute Input: Meeting name, agenda, attendees Output: Context brief per agenda item; stakeholder notes; decisions needed; meeting rules from work.local.md culture
DURING (real-time support): Purpose: Structured note-taking format that produces synthesis-ready output Output: Note-taking template with D/A/F/Q/R coding
AFTER (within 2 hours post-meeting): Purpose: Convert raw notes into structured, distributable meeting record Input: Raw notes (any format) Output: Decisions (with owners), actions (with owners and deadlines), deferred items (with triggers), next meeting proposal, work.local.md update proposals
STEP 3 — EXECUTE
Meeting Prep Output Structure
MEETING BRIEF — [Meeting Name]
Date: [Date] | Time: [Time + Timezone] | Duration: [Duration]
Attendees: [List — with roles from work.local.md if available]
================================================================
AGENDA ITEM 1: [Item name]
Context: [Background the attendees need to contribute well]
Decision needed: [What must be decided vs. what is for discussion only]
Your position: [What you think; what you want from this item]
Key risk: [What could go wrong in this discussion]
Stakeholder note: [Relevant communication guidance for key attendees]
[Repeat for each agenda item]
STAKEHOLDER NOTES:
[Person] ([Role]): [Communication style; what they need to engage;
what they are likely to push on; how to handle]
MEETING RULES (from work.local.md):
[Apply culture.decision_making; culture.unwritten_rules relevant to this group]
LAST TIME THIS GROUP MET:
[Date; key decisions made; open actions from that meeting]
[Surface from decision log in work.local.md]
WHAT YOU NEED FROM THIS MEETING:
[Restate the user's goal — decision / alignment / update / commitment]
================================================================
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.
- 9d ago First seen · 153 lines · 84 tokens per session scan A 216588507955
meeting-intelligence is a skill published in the GitHub repository panaversity/agentfactory-business-plugins (29 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,180 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.
Other skills, from other repositories
memory-manage
Manage Active updates, atomic session handoffs, and human-gated Long-Term proposals. Use when asked to remember something, preserve a decision, log meaningful work, or review memory.
apple-platform-automation
Manage Apple/macOS personal apps and device workflows from Hermes, including Apple Notes, Reminders, Find My, iMessage/SMS, and UI automation. Use when a task involves Apple ecosystem data or macOS-only CLIs such as memo, remindctl, imsg, FindMy.app, AppleScript, or Peekaboo.
wrap-up
End-of-session confidence and blind-spot review. Use when closing meaningful work, before committing to action, or when the user asks what is missing or uncertain.
dashboard-module-builder
Use this skill when the user asks Manor AI to add, edit, remove, rearrange, or personalize content on their Dashboard, including requests such as "show daily news", "add a stock module", or "change this dashboard module". It generates a private, loadable Dashboard module and submits it for live preview instead of…
ibrahim-daily-briefing
Generate Ibrahim's daily briefing — new leads, upcoming calls, pending follow-ups, system health, and one strategic question. Delivered to Telegram.
mcp_gmail
Operate the user's connected Gmail account through the Gmail MCP. Use when the user asks to read, search, summarize, send, reply to, draft, label, archive, or triage email on their Gmail / Google Mail account.