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 agentmods add skills/ooiyeefei/ccc/scribenpx skills add ooiyeefei/ccc --skill scribegit clone --depth 1 https://github.com/ooiyeefei/cccWhat 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 | $0.00050 | $0.00779 |
| Opus 5 | $0.00025 | $0.00390 |
| Sonnet 5 | $0.00010 | $0.00156 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
scribe 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 2d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A summariser cannot hear. Hand it a garbled span and it launders the noise into a clean fact — "some paying customer" becomes "~8,000 users" — and nothing downstream can separate that from a real figure. This skill holds a chain of custody: every claim in the notes traces back to audio the model actually heard well, and a claim whose custody is broken says so.
Scripts are in scripts/. They need a Python with httpx; --provider local also needs faster-whisper.
1. Establish the recording's context
Ask the user, or read it off the surrounding material — the files, the repo, an existing transcript:
- Language — the ISO code to pin. Left unpinned, per-window detection flaps on code-switched speech and the decoder emits fluent, confident text in the wrong language.
- Speakers — how many, plus names and roles.
- Domain vocabulary — product names, people, companies, jargon, currencies.
Done when you can state the language code, the speaker count, and at least five domain terms.
2. Transcribe
python scripts/transcribe.py AUDIO... --out DIR --language <code> --diarize --keyterms "term,term,..."
--provider selects the backend; auto takes the first with a key present. Setup, capability and cost per provider: references/providers.md.
Prefer a backend that diarizes. Speaker labels you derive yourself by reasoning about who-said-what are inference, and inference is a break in the chain of custody.
Done when every input file has a .json and .txt in DIR.
3. Audit before you read
python scripts/audit.py DIR
Two findings, both of which vanish once a transcript is flattened into prose:
- Gaps — a recorder stopped mid-meeting drops content in silence, and the notes that follow read as complete. A gap is a stretch of the meeting you hold no evidence for.
- Risky spans — numbers and proper nouns resting on low-confidence audio. This is where laundering happens.
Give every finding one of three dispositions: corroborated against a second transcript, confirmed with the user, or carried into the notes as a marked uncertainty.
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.
- 2d ago First seen · 69 lines · 50 tokens per session scan A 45eab86fa713
scribe is a skill published in the GitHub repository ooiyeefei/ccc (483 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 779 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 2026-08-30.
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