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 hassancs91/claude-youtube-editor --skill clean-audiogit clone --depth 1 https://github.com/hassancs91/claude-youtube-editorWrote 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/hassancs91/claude-youtube-editor/clean-audio)<a href="https://agentmods.dev/skills/hassancs91/claude-youtube-editor/clean-audio"><img src="https://agentmods.dev/badge/skills/hassancs91/claude-youtube-editor/clean-audio/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/hassancs91/claude-youtube-editor/clean-audio"><img src="https://agentmods.dev/badge/skills/hassancs91/claude-youtube-editor/clean-audio.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 | $0.00196 | $0.01856 |
| Opus 5 | $0.00098 | $0.00928 |
| Sonnet 5 | $0.00039 | $0.00371 |
| Haiku 4.5 | $0.00020 | $0.00186 |
Grade A, and why
clean-audio 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 11d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
clean-audio — voice cleanup
Take a locked master cut and remove its background noise, producing a cleaned master whose voice sounds natural and whose visuals are untouched. Runs early (once the cut is locked) so everything downstream — TSX bake, SFX mix, final assemble — sits on the clean voice. Work with the user; the final loudness/limiting is the final-mix step's job, this step is "denoise only, levels preserved."
The engine is tools/clean_voice.py; this skill is the judgment around it: diagnose → pick method
→ A/B → clean → rewire.
The two methods (pick by NOISE TYPE — this is the core decision)
| Method | What it is | Use when | Cost |
|---|---|---|---|
--method eleven |
ElevenLabs Voice Isolator (cloud ML voice/noise separation) | Dynamic, broadband noise in the voice band — outdoor running water, wind, traffic, crowd, cafe. Local tools CANNOT remove these. | ELEVENLABS_API_KEY |
--method rnnoise --model sh (or cb) |
Local RNNoise via ffmpeg arnndn (models in tools/models/rnnoise/) |
Stationary / mild noise (steady hiss, fan, some room tone). Free/offline. Only PARTIALLY removes dynamic noise. | free |
Proven on video-1 (shot outdoors with a stream): afftdn did ~nothing, RNNoise only partially darkened
the water bed, ElevenLabs removed it near-completely (pauses to near-silence, voice + breaths intact).
Rule of thumb: stationary noise → try local first; dynamic broadband (water/wind/traffic) → ElevenLabs.
Inputs (read/measure first, every time)
- The master —
videos/video-N/reference/<cut>.mp4(or the locked cut). Original is NEVER modified; output is a new-clean/-clean-<model>file. - The composited preview (if it exists) —
videos/video-N/output/video-N-preview.mp4, to make a clean in-context preview by swapping audio (its video is identical — no re-bake needed). videos/video-N/work/timeline.json— itsmasterfield; you rewire this to the clean master on approval.
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.
- 11d ago First seen · 88 lines · 196 tokens per session scan A 6ee26f13dfbf
clean-audio is a skill published in the GitHub repository hassancs91/claude-youtube-editor (303 stars, last pushed 23d ago), licensed MIT. It adds 196 tokens to every session and 1,856 once invoked, about $0.0010 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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