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 ooiyeefei/ccc --skill scribegit clone --depth 1 https://github.com/ooiyeefei/cccWrote 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/ooiyeefei/ccc/scribe)<a href="https://agentmods.dev/skills/ooiyeefei/ccc/scribe"><img src="https://agentmods.dev/badge/skills/ooiyeefei/ccc/scribe/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/ooiyeefei/ccc/scribe"><img src="https://agentmods.dev/badge/skills/ooiyeefei/ccc/scribe.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 | $0.00050 | $0.00836 |
| Opus 5.5 | $0.00020 | $0.00334 |
| Sonnet 5.5 | $0.00010 | $0.00167 |
| Haiku 4.5 | $0.00005 | $0.00084 |
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 3d 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> --keyterms "term,term,..."
--provider local is the default; auto is a compatibility alias for local. Existing API keys do not trigger an upload. Select elevenlabs, deepgram or openai explicitly when hosted transcription is appropriate. Setup, capability and cost per provider: references/providers.md.
Start with local faster-whisper. It does not diarize, so speaker attribution stays uncertain unless corroborated. When speaker labels are required, choose an authorised hosted diarizing provider explicitly and add --diarize. A missing local dependency fails without switching to a hosted provider. Pre-cache weights for fully offline use.
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:
What ships with it
6 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.
- 3d ago Changed e8e89d1dc8bb
- 1mo ago First seen · 69 lines · 50 tokens per session scan A 45eab86fa713
scribe is a skill published in the GitHub repository ooiyeefei/ccc (496 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 836 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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