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 Wang-Cankun/cankun-skills --skill meeting-audio-reportgit clone --depth 1 https://github.com/Wang-Cankun/cankun-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/wang-cankun/cankun-skills/meeting-audio-report)<a href="https://agentmods.dev/skills/wang-cankun/cankun-skills/meeting-audio-report"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/meeting-audio-report/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/wang-cankun/cankun-skills/meeting-audio-report"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/meeting-audio-report.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 | $0.00097 | $0.00945 |
| Opus 5 | $0.00048 | $0.00473 |
| Sonnet 5 | $0.00019 | $0.00189 |
| Haiku 4.5 | $0.00010 | $0.00094 |
Grade A, and why
meeting-audio-report 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 12d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting audio report
A recording becomes two things: a verbatim transcript, and a report someone who missed the meeting can act on.
Two words govern the run.
Coverage — did every second of audio reach the transcript? Transcription drops blocks on transport errors, and the loss is silent. The one block lost is as likely as any other to hold the decisive sentence. Coverage is a gate, not a statistic.
Fidelity — does every name, number, and quote in the report trace to the audio? A transcription model guesses at proper nouns; a summarising model guesses at who said what. Both guesses arrive fluent and confident. The report carries what the recording supports and flags the rest.
1. Transcribe
scripts/transcribe.py <audio-or-video> [--out-dir DIR]
Writes <stem>-transcript.md, <stem>-coverage.json, and <stem>-blocks.json beside the input, or into --out-dir.
Done when the script has exited and the manifest exists.
2. Pass the coverage gate
The script exits non-zero when any block failed, and names each gap with its time range.
scripts/transcribe.py <audio> --retry-missing [--out-dir DIR]
Repeat until it exits zero. Failures are transport, not audio, so a second attempt usually lands.
Done when blocks_ok equals blocks_total in the manifest. Carry which blocks were recovered into step 5 — a recovered block that turned out to hold something material is itself a finding.
A gap that survives repeated recovery goes into the report as a stated gap, with its time range and what was being discussed either side of it. Never write around it silently.
references/transcription.md holds the manifest format, tuning flags, cost, and the speaker-label limits.
3. Read the whole transcript
Read it end to end before writing anything. An hour of meeting is 40 to 60 KB — read it in full, not in excerpts.
Done when you can state, without looking back: the subject, every number that was quoted, what was decided, what was deferred, and what a speaker said that contradicts a document.
What ships with it
5 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.
- 12d ago First seen · 83 lines · 97 tokens per session scan A 7a70eb9d8955
meeting-audio-report is a skill published in the GitHub repository Wang-Cankun/cankun-skills (2 stars, last pushed 15d ago), licensed MIT. It adds 97 tokens to every session and 945 once invoked, about $0.0005 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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