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 fanfan-de/anybox --skill google-calendargit clone --depth 1 https://github.com/fanfan-de/anyboxWrote 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/fanfan-de/anybox/google-calendar)<a href="https://agentmods.dev/skills/fanfan-de/anybox/google-calendar"><img src="https://agentmods.dev/badge/skills/fanfan-de/anybox/google-calendar/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/fanfan-de/anybox/google-calendar"><img src="https://agentmods.dev/badge/skills/fanfan-de/anybox/google-calendar.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 | $0.00066 | $0.01326 |
| Opus 5 | $0.00033 | $0.00663 |
| Sonnet 5 | $0.00013 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
google-calendar 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Calendar
Overview
Use this skill to turn raw calendar data into clear scheduling decisions, reminder plans, and safe event updates. Keep answers grounded in exact dates, times, and calendar evidence.
Preferred Deliverables
- Availability summaries with exact candidate slots, timezone, and conflicts.
- Room or resource recommendations grounded in actual calendar availability or prior meeting patterns.
- Event change proposals that show the current event and the intended update.
- Reminder change proposals with exact lead time, scope, and the meetings that qualify.
- Temporary hold proposals or final hold details with the exact slot and whether the hold is transparent.
- Final event details that are ready to create or confirm.
Workflow
- Read the relevant calendar state first so the request is grounded in actual events, calendars, and time windows.
- Normalize relative time language into explicit dates, times, and timezone-aware ranges before reasoning about availability.
- Keep reads bounded. Use explicit
time_minandtime_maxwhenever possible, avoid unbounded broad searches, and choose a small default window when the user does not state one. - When a bounded search returns too much, page within that same window before widening the date range. For longer historical, precedent, or preference discovery, chunk the search into smaller windows instead of one giant pull.
- When the user leaves something ambiguous, inspect previous calendar data for a clear precedent before choosing a default. Follow established patterns when they are obvious, such as using the user's usual meeting duration if similar events are consistently 30 minutes.
- When a participant, attendee list, manager, room, or contact detail is referenced indirectly, search a bounded relevant window and read the likely source event before asking the user for details. Use
read_eventorsearch_events_all_fieldswhen attendee emails or full event metadata matter; the standardsearch_eventssummary is not enough for contact details. - If a found event belongs to a recurring series and the user wants a series-level change, read the master series or recurrence details before editing. Do not infer the cadence or scope from a single occurrence when
recurring_event_idor equivalent series evidence is available. - Use the connector's
update_eventaction for recurring edits and setupdate_scopetothis_instance,entire_series, orthis_and_followingas needed. If the series is COUNT-based and the tool requires an explicit recurrence for a future-only split, preserve and restate the recurrence rule or explain that the connector cannot safely infer the split from one occurrence. - For room-finding requests, do not assume there is a reliable global room search. In this V1 flow, mine a reasonable window of past meetings, locations, and resource attendees to build a candidate room list, then compare availability on that concrete set.
- For bulk reminder or classification-based edit requests, inspect a reasonable upcoming window first instead of asking for extra scoping immediately. If the prompt does not bound the horizon, use a narrow default such as the next 30 days and say so.
- When notes, prep context, or missing details matter, inspect the event payload before proposing a change.
- For free-slot and temporary-hold requests, if the prompt already gives the window and duration, search within that range and move directly to proposing or placing the hold. Prefer a transparent hold for temporary placeholders unless the user clearly wants a blocking focus block.
- Surface conflicts, transparent holds, and missing meeting details before suggesting a write.
- If the request is still ambiguous after checking for precedent or scanning a reasonable bounded window, summarize the candidate slots or exact diff before writing anything.
What ships with it
3 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 · 67 lines · 66 tokens per session scan A bd29e83353cd
google-calendar is a skill published in the GitHub repository fanfan-de/anybox (57 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,326 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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