--- name: transcript-cleanup description: This skill should be used when the user asks to "clean up a transcript", "fix speech artifacts", "edit interview quotes", "polish transcription", "clean up quotes from a recording", or when working with speech-to-text output that needs light editing while preserving the speaker's voice. --- # Transcript cleanup Clean speech-to-text artifacts from transcripts and direct quotes without editorializing. The goal is readability, not rewording. Preserve the speaker's vocabulary, sentence structure, and personality. Fix only what a competent transcriptionist would fix. ## The rule If you cannot point to a specific speech artifact or transcription error, do not change the sentence. "Sounds better" is not a reason to edit. Every edit must fall into one of the pattern categories below. ## Pattern categories ### 1. Filler "like" Spoken English uses "like" as a verbal pause. Remove it when it serves no grammatical function. Keep it when it means "such as" or "similar to." | Before | After | Why | | --------------------------------------------------- | --------------------------------------------------- | ------------------------- | | has like a road map | has a road map | filler before article | | is like no more than three digits | is no more than three digits | filler before adverb | | was like AI is important | where the message was AI is important | filler replacing a clause | | a mega corp like Apple | a mega corp like Apple | means "such as" -- keep | | a generic topic like generative engine optimization | a generic topic like generative engine optimization | means "such as" -- keep | **Test:** Remove "like" and read the sentence. If it still makes grammatical sense and the meaning is unchanged, the "like" was filler. ### 2. Spoken grammar Speakers routinely break grammar rules that readers notice on the page. Fix subject-verb agreement and comparison constructions. | Before | After | Rule | | --------------------------- | ------------------------- | -------------------------------------------- | | there's so many signals | there are so many signals | subject-verb agreement ("signals" is plural) | | as powerful than if we had | as powerful as if we had | "as...as" comparison, not "as...than" | | as much of a signal than if | as much of a signal as if | same pattern | **Test:** Read the sentence aloud slowly. If the grammar error is obvious when spoken deliberately rather than quickly, fix it. ### 3. Word order from natural speech Speakers front-load or rearrange words in ways that read awkwardly on paper. Restore standard English word order. | Before | After | | -------------------------------------- | --------------------------------- | | put in specifically keywords | specifically put in keywords | | that's like deep on a bottom of funnel | that's deep on a bottom-of-funnel | ### 4. Orphan words Mid-sentence restructuring during speech leaves behind words that no longer connect to anything. Remove them. | Before | After | Orphan | | ------------------------------- | ------------------------- | ------------------------------------------ | | things that while seem exciting | things that seem exciting | "while" left over from an abandoned clause | ### 5. Transcription artifacts Speech-to-text engines sometimes drop words, merge sentences, or mishear connectives. Restore the minimal missing words needed for the sentence to parse. | Before | After | Fix | | ------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ------------------------------------------------- | | was like AI is important | where the message was AI is important | restored dropped clause | | who is higher up in the organization chart like a chief marketing officer | who is higher up in the organization chart, like a chief marketing officer | added comma before "like" (parenthetical example) | Only add words when the sentence is genuinely unparseable without them. If the meaning is clear despite missing words, leave it alone. ## Process 1. Read the full transcript or quote set before editing anything. 2. Identify each artifact by pattern category (1-5 above). 3. Make the smallest edit that fixes the artifact. 4. Re-read the edited version aloud. If the speaker's voice is gone, revert. 5. When in doubt, leave it. A slightly rough quote is better than a polished one that no longer sounds like the person who said it. ## What not to touch - Informal vocabulary ("gonna", "kind of", "a lot") -- these are voice, not errors - Sentence fragments that work as emphasis -- speakers use these deliberately - Repeated words used for emphasis ("it's really, really important") - Regional or personal speech patterns -- do not normalize dialect - Content or meaning -- never change what the speaker said, only how clearly the transcription conveys it - Sentences that are merely inelegant -- ugly-but-clear is fine ## Working with direct quotes When cleaning quotes embedded in documents (blockquotes, inline quotes), apply extra caution. The reader knows this is a quote from a real person. Overcleaning makes quoted speech sound ghostwritten. For attributed quotes (with a speaker name), preserve more roughness. The attribution signals "this is how they actually talk." For unattributed quotes used as pull-quotes or callouts, slightly more cleanup is acceptable since the reader expects polished text. ## Batch editing workflow When cleaning an entire transcript or document with many quotes: 1. Grep for all direct quotes across the file set. 2. Categorize each fix by pattern (1-5) before editing. 3. Apply fixes file by file, not pattern by pattern, to maintain reading context. 4. Flag any borderline cases for human review rather than guessing.