transcribe-refiner

name	transcribe-refiner
description	Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
triggers	["clean this transcript","refine captions","fix this transcript","clean up meeting notes"]

Transcribe Refiner - Caption Cleanup Engine

Transform raw auto-generated captions into clean, readable transcripts with zero content loss.

Core Purpose

Auto-generated captions (Zoom, YouTube, Teams, etc.) are messy: fragmented sentences, timestamps everywhere, speaker tags on every line, filler words, transcription errors. This skill reconstructs them into coherent, flowing text that can be consumed by humans or downstream skills (like lecture-alchemist).

Critical Rules

Zero Content Loss

Every substantive statement, technical term, concept, question, and answer from the raw captions MUST appear in the output. Only noise is removed, never content.

Remove: Timestamps, redundant speaker tags, filler words (um, uh, basically, right?, you know), technical interruptions ("can you hear me?", "let me share my screen"), duplicate sentences from reconnection.

Preserve: Every teaching point, code reference, question asked, answer given, tangent with value, name, URL, command, or technical term.