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 seb1n/awesome-ai-agent-skills --skill meeting-schedulergit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/meeting-scheduler)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/meeting-scheduler"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/meeting-scheduler/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/seb1n/awesome-ai-agent-skills/meeting-scheduler"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/meeting-scheduler.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 | $0.00051 | $0.01669 |
| Opus 5 | $0.00026 | $0.00834 |
| Sonnet 5 | $0.00010 | $0.00334 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
meeting-scheduler 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Meeting Scheduler — 97% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Scheduler
This skill enables an AI agent to handle end-to-end meeting coordination. Given a natural language request, the agent identifies participants, resolves time zone differences, queries calendar availability, proposes conflict-free time slots, dispatches calendar invitations, and tracks RSVPs. It integrates with Google Calendar, Microsoft Outlook, and Calendly to cover the most common enterprise and freelance scheduling workflows.
Workflow
-
Parse the Meeting Request Extract structured data from the user's natural language input. Identify all required participants (by name, email, or team alias), the meeting topic or agenda, the desired duration, and any date or time constraints such as "next Tuesday afternoon" or "before end of sprint." Flag any ambiguous references for clarification before proceeding.
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Resolve Time Zones Look up each participant's configured time zone from their calendar profile or contact record. Convert all proposed windows into each participant's local time so that comparisons are accurate. When a participant's time zone is unknown, prompt the organizer to confirm it. Display all suggested times with explicit time zone labels (e.g., "3:00 PM EST / 12:00 PM PST") to avoid confusion.
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Check Calendar Availability Query each participant's calendar through the appropriate integration (Google Calendar API, Microsoft Graph API, or Calendly availability endpoint). Collect busy blocks for the requested date range and compute the intersection of free windows. Respect each participant's working-hours preferences and any configured focus-time or no-meeting blocks. Apply a configurable buffer (default 10 minutes) between adjacent meetings to allow transition time.
-
Propose Optimal Time Slots Rank the available windows using a scoring heuristic: prefer slots that fall within standard working hours for all participants, minimize the number of early-morning or late-evening local times, and favor earlier dates when urgency is indicated. Present the top three ranked options to the organizer with a summary showing each participant's local time.
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 · 119 lines · 51 tokens per session scan A 105ec72b8032
meeting-scheduler is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 1,669 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 2026-09-03.
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