我長期處理中英夾雜的技術型影音內容（教學影片、會議與簡報錄音），很清楚這類素材最頭痛的就是專有名詞和英文縮寫被聽錯。這個 Skill 把我實際在用的整套做法工具化：以 faster-whisper 在本機 CPU 就能轉錄（免 GPU、免雲端 API、免費），並用「轉錄前餵專有名詞提示＋轉錄後批次修正同音錯字」兩道工序把準確率拉高；YouTube 連結則走「先抓現成字幕、失敗自動改抓音訊轉錄」的穩定路線，最後一併產出 SRT 字幕、逐字稿與帶時間戳的重點摘要。全程本機運算、檔案不上雲，適合在意內容隱私又要快速消化長影音的人。 I work daily with mixed Chinese-English technical audio, where jargon and acronyms get mis-heard most. This Skill packages my workflow: faster-whisper runs locally on CPU (no GPU, no cloud API)
Upload your mix or master and get a real technical diagnostic - loudness, frequency balance, dynamics, stereo image - turned into a prioritized, DAW-agnostic action plan.
Cuts background noise (AC, fans, electrical hum) and softens sibilance in meeting, lecture, and interview recordings and video, then normalizes loudness to podcast (-16), YouTube (-14), or broadcast (-23 LUFS). Point to a quiet patch and profile mode removes only that noise. Also trims leading/trailing silence and builds a before/after report (spectrogram, LUFS). On a 60-second 1962 Kennedy clip, the noise floor fell 5.6 dB and loudness moved from -17.1 to -15.9 LUFS. Only ffmpeg, no API key.
【Lifetime Buyout · 100% Offline】Industrial batch voice & de-breath engine for YouTube Shorts, TikTok & podcast creators! Drag & drop an entire folder of audio/video to cut silent gaps and breath pauses in 0.3s. Features 10ms anti-click cosine crossfade, 50ms consonant buffer to protect word endings, and -14 LUFS mastering. Bundled standalone FFmpeg, 1-click Windows launcher, zero token costs, and 100% local privacy. Ditch tedious Audition timeline slicing forever!
Find generic AI residue in blogs, social posts, newsletters, and marketing copy, then repair it back into your own voice. This agent flags template-like phrasing, hollow CTAs, fake certainty, and over-polished structure, then gives a clearer, more specific rewrite focused on authentic voice and specificity, not artificial detection outcomes.
文章の印象を整える作業を効率化するために設計したスキルです。『意味は変えずにトーンだけ変える』ことを最優先に、命令調をやわらげるクッション言葉、ビジネス文書の簡潔な敬体、冗長表現の削除、SNS向けの改行とフックなど、目的別の書き換えパターンを整理しました。締切・金額・要件などの核を保持したまま、複数トーンを並べて比較でき、どこをなぜ変えたかも提示することを狙いにしています。