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 mpaarating/ai-workflow-kit --skill meeting-prepgit clone --depth 1 https://github.com/mpaarating/ai-workflow-kitWrote 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/mpaarating/ai-workflow-kit/meeting-prep)<a href="https://agentmods.dev/skills/mpaarating/ai-workflow-kit/meeting-prep"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/meeting-prep/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/mpaarating/ai-workflow-kit/meeting-prep"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/meeting-prep.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 | $0.00019 | $0.01039 |
| Opus 5.5 | $0.00008 | $0.00416 |
| Sonnet 5.5 | $0.00004 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
meeting-prep 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 2mo 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Prep
Trigger Phrases
- "prep for [meeting]"
- "prepare for [meeting]"
- "meeting prep"
- "get ready for [meeting]"
Inputs
The user provides a meeting name, topic, or attendee name. This can be:
- An exact meeting title: "prep for Sprint Retrospective"
- A person's name (implies their next 1:1): "prep for my meeting with Sarah"
- A topic: "prep for the API migration discussion"
Workflow
Step 1: Find the Meeting
Search {{calendar}} for upcoming events matching the user's input.
- Match by title, attendee name, or description keywords
- If multiple matches, show a numbered list and ask which one
- If no matches, ask the user to clarify the meeting name or date
- Extract from the matched event:
- Title
- Date and time
- Attendees (names and roles if available)
- Description/agenda (if present in the calendar event)
- Recurring? (note if this is a recurring meeting — previous notes are more likely to exist)
Step 2: Find Previous Meeting Notes
Search {{notes}} for notes from prior instances of this meeting.
- Search by meeting title, attendee names, and topic keywords
- Look for the most recent 2-3 instances
- Extract:
- Action items from the last meeting (especially any assigned to the user)
- Open questions or decisions that were deferred
- Topics that were tabled for follow-up
- If no previous notes exist, note this and move on
Step 3: Find Related Tickets
Search {{tasks}} for tickets related to the meeting topic.
- Use the meeting title, description keywords, and attendee names as search terms
- Filter to tickets that are:
- Assigned to the user or attendees
- Recently updated (last 2 weeks)
- In active states (not closed/done)
- Extract ticket ID, title, status, and assignee for each match
Step 4: Find Related Documents
Search {{notes}} more broadly for documents related to the meeting topic.
- Search by topic keywords, project names, or technical terms from the meeting description
- Look for: design docs, RFCs, decision records, relevant wiki pages
- Keep to the top 3-5 most relevant results
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.
- 2mo ago First seen · 121 lines · 19 tokens per session scan A 28f6dd2f8a38
meeting-prep is a skill published in the GitHub repository mpaarating/ai-workflow-kit (2 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 1,039 once invoked, about $0.0001 per session on Opus 5.5. 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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