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/gaotiexinqu/oneresearchclaw/audio_structuringnpx skills add gaotiexinqu/OneResearchClaw --skill audio_structuringgit clone --depth 1 https://github.com/gaotiexinqu/OneResearchClawWrote 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/gaotiexinqu/oneresearchclaw/audio_structuring)<a href="https://agentmods.dev/skills/gaotiexinqu/oneresearchclaw/audio_structuring"><img src="https://agentmods.dev/badge/skills/gaotiexinqu/oneresearchclaw/audio_structuring.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00259 |
| Opus 5 | $0.00000 | $0.00130 |
| Sonnet 5 | $0.00000 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
audio_structuring 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 6d 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.
What it actually says
Audio Structuring Skill
Purpose
Convert a meeting audio file into a dialogue-style transcript txt file for downstream skills.
Input
audio_path: path to a single audio fileoutput_dir: directory to save the transcript- optional
transcription_language: language code such asenorzh
Output
meeting_transcript.txt
Behavior
- Reuse WhisperX as the transcription backend.
- Reuse local offline model directories instead of downloading models from Hugging Face at runtime.
- Enable diarization so the txt contains speaker labels whenever available.
- Automatically convert Traditional Chinese output to Simplified Chinese when language is set to
zh,zh-CN, orzh-TW. - Do not implement custom ASR, alignment, or diarization logic.
- Do not generate summary, action items, reports, or any extra files.
- This skill only produces transcript txt for downstream skills.
Local model requirements
The repository is expected to provide these local model directories under models/:
models/faster-whisper-large-v2models/speaker-diarization-community-1
Run
bash scripts/run.sh <audio_path> <output_dir> [language]
What ships with it
1 file 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.
- 6d ago First seen · 31 lines · 0 tokens per session scan A 7944ea905589
audio_structuring is a skill published in the GitHub repository gaotiexinqu/OneResearchClaw (445 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 259 tokens. 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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