undefined-ui / second-brain-transcript
Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording text, or asks to process a video or audio source. Do NOT use for text that is already prose, for summarising a transcript, or for the ingestion itself.
Clean a transcript
Concept pages built from unpunctuated caption dumps are noticeably worse,
because the model spends its attention reconstructing sentence boundaries
instead of understanding claims.
Core rule
Clean, never summarise. Nothing is cut. This is a formatting pass on the record,
and the record has to stay faithful.
Workflow
Punctuate and paragraph. Sentence boundaries first, then paragraphs at
topic shifts.Label speakers where they change. In an interview this is most of the
value.Fix technical terms. Auto-captions mangle proper nouns and jargon
consistently. Flag anything you could not resolve rather than guessing.Keep coarse timestamps every few minutes, so a claim on a wiki page can
be verified in ten seconds instead of a rewatch.Propose split points if the recording covers separate topics. A
three-hour podcast is not one source.- Write frontmatter: title, channel or speaker, URL, date.
Output format
The cleaned transcript, then:
Cleaned: <source>
Speakers labelled: <n>
Terms corrected: <list>
Uncertain: <list of what could not be resolved>
Suggested splits: <topics with timestamps, or none>Calibration
Never drop filler that carries meaning, such as hedging or a speaker correcting
themselves. Those are exactly what distinguish a claim from an aside.
If the transcript is too garbled to clean reliably, say so and recommend
re-pulling it rather than producing a plausible reconstruction.