Talk Coach: A Sharper Talk After Every Rehearsal

What it does

You can't hear your own rehearsal the way the room will. You know the results too well to notice they went past at twice the speed of the rest, you know what your acronyms mean, and the minute you ran over felt like twenty seconds. This skill makes your agent listen for you: it transcribes your rehearsal on your own machine, measures what the recording shows, and reads the talk as your audience will hear it.

• Where the minutes went. Time against your slot, and each section's start, length, share and pace, so a rushed results section or a methods section that ate the talk shows up as numbers with timestamps.
• A cut list that adds up. When you're over, concrete passages to cut, each with its timestamp and the seconds it saves at your own pace, totalling the overrun plus a margin. Never "speak faster".
• The talk as your audience hears it. When the main message is first said, which terms and acronyms are never explained for this audience, how many numbers come in one breath, and whether the last sentence before the thanks is your take-home.
• Verbal habits, counted and located. Hesitation sounds, filler phrases judged in context, sentences about the talk ("now I'll show you the results"), hedges on the main result, each with its timestamp.
• Three priorities, not thirty remarks. Each with the evidence, the change, a drill and a target the next rehearsal can be checked against. Rerun on the next recording and compare.
• Feedback grounded in what was recorded. Every point quotes the transcript with a timestamp. Misheard terms are corrected against your outline and never counted against you, and nothing is said about your voice or confidence that a transcript can't show.

What a run looks like

In our demo the agent got a made-up five-minute research-day talk, spoken by a computer voice, with rehearsal problems planted, plus the slide outline. It transcribed the recording locally and found all of them: 6:29 against a 5-minute slot, a results section at 214 words per minute against 126 for the rest, SOH, ICA and DCIR never explained to a mixed department audience, and three hesitations a minute. It wrote twelve timed cuts adding up to 135 seconds, three priorities with targets, and set aside ten recogniser errors ("U square" for R squared, "AS" for EIS) instead of blaming the speaker for them.

Settings

Conference talks, lightning and research-day talks, job talks and seminars, thesis defence and viva presentations, lab meetings and progress reviews, online talks. English and Spanish.

What's included

• SKILL.md: the ten-step method and its grounding rules
• scripts/transcribe.py: local transcription with word timestamps (faster-whisper, mlx-whisper, openai-whisper or whisper.cpp)
• scripts/import_transcript.py: captions (.vtt, .srt) or text instead of a recording
• scripts/delivery_metrics.py: the measures, per section, with timestamps and rerun comparison (standard library)
• references/: transcription setup per OS with model sizes and speeds, a delivery guide by setting, drills with targets, the feedback format
• examples/: the demo's transcript, captions, outline and the feedback it produced

Part of the Aurora Lab research pipeline

Works on its own, on any talk. Pairs well with Aurora Lab's Q&A Prep and Poster Layout; needs neither.