transcript-conditioning

Resolve speaker labels, normalize ASR-garbled entities, and extract confidence-tagged quotes from raw event transcripts.

1|Updated Apr 9, 2026
One-click install
npx skills add https://github.com/AlexYedi/Empire_State_Events_Pipeline_Take_3 --skill transcript-conditioning-alexyedi
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: transcript-conditioning
Source: https://github.com/AlexYedi/Empire_State_Events_Pipeline_Take_3/tree/main/.claude/skills/transcript-intelligence/transcript-conditioning
Command: npx skills add https://github.com/AlexYedi/Empire_State_Events_Pipeline_Take_3 --skill transcript-conditioning-alexyedi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Raw event transcripts from Otter, Zoom, or phone recordings arrive with unreliable diarized speaker labels and ASR-mangled proper nouns, which causes downstream content to misattribute quotes and publish garbled company or product names. ## Core Features & Use Cases - Speaker Resolution: Maps anonymous "Speaker N" labels to a known event roster using content tells like company self-references and domain vocabulary, with per-resolution confidence. - Entity Normalization Glossary: Builds a mangled-to-canonical map for companies, people, products, and acronyms anchored to the pre-event brief, excluding unresolvable garbles from quotes. - Confidence-Tagged Quote Bank: Extracts verbatim quotes attributed to resolved speakers with HIGH/MED confidence tags and an overall conditioning confidence score. - Use Case: After a five-speaker NYC panel recorded on a phone, paste the raw transcript to get a speaker resolution table, entity glossary, and a 12-quote bank that feeds directly into post-event content drafting. ## Quick Start Condition this pasted event transcript against the pre-event brief and produce a speaker resolution table, entity glossary, and confidence-tagged quote bank.

Frequently Asked Questions about transcript-conditioning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fix speaker labels in an Otter or Zoom transcript?

Resolve speakers by content rather than by the diarized labels. Match each voice to a known roster using role tells like company self-references, domain vocabulary, and moderator question patterns, then record confidence per resolution and flag voices that cannot be resolved.

How to clean up ASR-mangled names in a transcript?

Build a mangled-to-canonical glossary anchored to a pre-event brief listing known companies, people, products, and acronyms. Only correct garbles that map confidently to a brief entity, and exclude unresolvable ones from any public quote rather than guessing.

When should I condition a transcript before writing event content?

Condition whenever the transcript comes from a manual paste, phone recording, or unreliable diarization and you plan to quote named people publicly. Skip it when the diarized transcript is already clean and the speaker roster is small and obvious.

Can I quote a low-confidence transcript section verbatim?

No. Quotes tagged MED or drawn from low-signal sections like mic dropouts or crosstalk should be paraphrased and softened, never promoted to verbatim quotes. Only HIGH-confidence lines are safe to quote word for word.

What is the difference between transcript conditioning and sales call transcript analysis?

Transcript conditioning is single-event and content-facing, resolving speakers and entities against a known roster to produce a quote bank. Sales call analysis mines corpora of ten or more calls for pains, triggers, objections, and battlecards.