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 mathematic-inc/earl --skill recall_aigit clone --depth 1 https://github.com/mathematic-inc/earlWrote 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/mathematic-inc/earl/recall_ai)<a href="https://agentmods.dev/skills/mathematic-inc/earl/recall_ai"><img src="https://agentmods.dev/badge/skills/mathematic-inc/earl/recall_ai/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/mathematic-inc/earl/recall_ai"><img src="https://agentmods.dev/badge/skills/mathematic-inc/earl/recall_ai.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 | $0.00043 | $0.01613 |
| Opus 5.5 | $0.00017 | $0.00645 |
| Sonnet 5.5 | $0.00009 | $0.00323 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
recall_ai 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 1mo 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall.ai
Records video meetings (Zoom, Google Meet, Teams, Webex, GoToMeeting), generates speaker-attributed transcripts, and produces downloadable audio/video files.
Status Reference
Bot lifecycle status (from get_bot):
| Status | Meaning |
|---|---|
pending |
Bot created, not yet attempting to join |
joining |
Bot is connecting to the meeting |
joined |
Bot is in the meeting, recording NOT started |
recording |
Bot is actively recording |
stopped |
Recording stopped, processing in progress |
done |
Bot left and all processing complete |
Artifact status (media_shortcuts.*.status.code):
| Status | Meaning |
|---|---|
waiting |
Not started |
processing |
Being generated |
done |
Ready to retrieve |
failed |
Generation failed — retry or contact support |
deleted |
Artifact was removed |
CRITICAL: Async Lifecycle
Creating a bot does NOT start recording. The full sequence is async:
create_bot → [wait: pending→joining→joined] → start_recording → [meeting runs] → leave_call/stop_recording → [wait: processing] → get_transcript → download_transcript
Do NOT call get_transcript immediately after create_bot. You will get an error or empty data. Always poll first.
Polling intervals:
- While status is
pendingorjoining: every 10–15 seconds (max 2 minutes — if longer, the meeting URL may be invalid or the meeting hasn't started) - While status is
recording: every 30 seconds - After
stop_recordingorleave_call, while transcript isprocessing: every 15 seconds
Recipe A: Record a meeting and get the transcript
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.
- 1mo ago First seen · 174 lines · 43 tokens per session scan A 284f6cebd224
recall_ai is a skill published in the GitHub repository mathematic-inc/earl (113 stars, last pushed 3d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,613 once invoked, about $0.0002 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 2026-08-30.
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Share a Google Docs document with edit access and email collaborators the link.
recipe-bulk-download-folder
List and download all files from a Google Drive folder.