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.
git clone --depth 1 https://github.com/ololand-ai/ololand-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/commands/ololand-ai/ololand-plugins/meeting-prep)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/meeting-prep"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/meeting-prep.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.01112 |
| Opus 5 | $0.00017 | $0.00556 |
| Sonnet 5 | $0.00007 | $0.00222 |
| Haiku 4.5 | $0.00003 | $0.00111 |
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 6d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Prep
Turn a deal's existing analysis into a tailored question list for management presentations, expert calls, customer references, or advisor sessions. Questions are deal-specific, not generic — each one links back to a risk, a document, or a pattern from a similar deal.
Usage
/meeting-prep <deal_id> <meeting_type> [focus]
Arguments
deal_id(required) — The deal the meeting is about.meeting_type(required) — One of:management,expert,customer,advisor.focus(optional) — Narrow the prep to a workstream:commercial,financial,operational,tech. Default: full business.
Execution
- Anchor the deal. Call
get_deal,get_financial_snapshot, andget_deal_summary_tilesto load company profile, current financial snapshot, and AI-generated executive summary / SWOT. - Pull the red flags. Call
get_deal_risks(severity="high")and (if any returned)get_deal_risks(severity="critical"). For each risk, callget_evidence_linksto attach the exact document + page that triggered it. These become the "must-ask, probe deeply" questions. - Topic-specific document search. Call
search_deal_documentswith queries matched tomeeting_typeandfocus:- management —
revenue concentration,customer churn,working capital seasonality,EBITDA bridge adjustments,org chart and key hires - expert —
competitive landscape,regulatory tailwinds,pricing power,substitution threats - customer —
contract terms,pricing history,support quality,competing vendors evaluated - advisor —
accounting policies,unusual treatments,restatement history,auditor changesPull 2-3 results per query — anything surprising becomes a question.
- management —
- Institutional pattern overlay. Call
find_similar_deals. For each similar deal, surface:- Risks that were systematically under-scored ("In 4/6 similar deals, customer concentration was flagged a tier too low")
- Questions that caught real issues post-close
- Valuation ranges to reference live (median EV/EBITDA, revenue multiples)
- Compose the one-page prep doc in this exact structure:
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.
- 6d ago First seen · 67 lines · 34 tokens per session scan A de5ba10eb911
meeting-prep is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 2d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,112 once invoked, about $0.0002 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 2026-08-31.
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