audition-clean-voice - SKILL.md Agent Skill

name: audition-clean-voice description: "Clean up a voice recording — remove hiss, hum, background noise, and harsh sibilance — by driving the real Adobe Audition app on a macOS host. Triggers: clean up audio, clean up the voice, remove hiss, remove hum, remove background noise, denoise, noise reduction, de-ess, reduce sibilance, repair voice, fix the recording. 中文触发词：降噪、去底噪、去杂音、去噪音、人声清理、清理录音、修复人声、去齿音、去咝声。" metadata: {"syll":{"emoji":"🎚️","os":["darwin"],"requires":{"bins":["ffmpeg"]}}}

Audition Clean Voice

Use the clean_audio_in_audition tool to clean up a voice recording — reducing hiss, hum, broadband noise, and harsh sibilance. This is not a filter that runs in this process — it drives the real Adobe Audition application on a macOS host, opens the clip, applies the cleanup, and exports the result.

When To Reach For This

The user wants a recording to sound cleaner. Recognize the intent from phrases like:

English: "clean up this audio", "clean up the voice", "remove the hiss", "remove the hum", "get rid of the background noise", "denoise this", "reduce the noise", "de-ess", "too much sibilance", "repair the voice", "fix the recording".
中文：「降噪」「去底噪」「去杂音」「去噪音」「帮我清理人声」「清理一下录音」「修复人声」「去齿音」「去咝声」。

How It Works

You call clean_audio_in_audition with the path to the source audio file.
The tool launches / focuses Adobe Audition, opens the clip, applies the noise reduction / de-ess chain, and exports the cleaned audio.
The tool measures the result (noise-floor reduction, whether the voice was preserved vs. only made louder) and returns a verdict.
The before and after audio render inline automatically — you do not need to attach or describe them yourself.

Confirm Before Control

This tool seizes the mouse and keyboard of the host machine. It MUST NOT take over the screen without the user's explicit permission.

First call — confirmed=false. Always make the first call with confirmed=false. The tool will return a takeover-consent question (it does not touch the mouse/keyboard yet). Surface that question to the user.
Wait for an explicit yes. Only proceed once the user clearly agrees — a "yes", "go ahead", "do it", "确认", "可以" in the conversation counts. Silence, ambiguity, or "maybe" does not count.
Second call — confirmed=true. Only then call again with confirmed=true. This is the call that actually takes over the host.

Never set confirmed=true on the first call, and never assume consent.

Honesty Protocol

Report only the verdict the tool returns. Do not embellish.

The tool returns an outcome such as excellent, pass, partial, gain_only, or failed, plus a measured score (e.g. noise-floor reduction).
If the verdict is excellent or pass, you may say the recording was cleaned / the noise was reduced.
If the verdict is partial, say the cleanup is incomplete / needs review — do not claim it is clean.
If the verdict is gain_only, the cleanup was not proven: the clip may only have been made louder, not actually denoised. Say so plainly — do not say "cleaned", "denoised", or "noise removed".
If the verdict is failed, say it failed and offer to retry or try a different clip.

Never claim "cleaned", "denoised", or "noise removed" beyond what the measured verdict supports. The user trusts the number, not your optimism.

Usage Protocol

When the user asks to clean a recording:

Confirm the source. Make sure you have the path to the audio the user wants processed.
First call, confirmed=false. Relay the takeover-consent question.
Get explicit consent. Wait for the user's "go ahead".
Second call, confirmed=true. Run the real cleanup.
Report the verdict honestly. State the outcome exactly as measured. The before/after audio renders inline on its own.

Example

User: "这段录音底噪好大，帮我降噪一下"

First, call clean_audio_in_audition(audio_path="/path/to/voice.wav", confirmed=false).
The tool replies with a consent question; relay it: "This will take over the mouse and keyboard on the Mac to run Audition. Shall I go ahead?"
User: "好的，开始吧"
Then call clean_audio_in_audition(audio_path="/path/to/voice.wav", confirmed=true).
Tool returns verdict gain_only. You report honestly: "处理跑完了，但校验显示只是音量变大、降噪效果未被证实，因此我不能说底噪已去除。要不要换个参数或换一段再试？" The before/after audio appears inline.

Do Not

Do not drive gui_action directly to attempt the cleanup — use this dedicated tool, which knows the Audition workflow and measures the result.
Do not call with confirmed=true before the user agrees.
Do not overstate the result. A gain_only verdict is not a cleanup.