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jbaruch/speaker-toolkit

Two-skill presentation system: analyze your speaking style into a rhetoric knowledge vault, then create new presentations that match your documented patterns. Includes an 88-entry Presentation Patterns taxonomy for scoring, brainstorming, and go-live preparation.

Overall
score

95%

Does it follow best practices?

Validation for skill structure

Overview
Skills
Evals
Files

download-commands.mdskills/rhetoric-knowledge-vault/references/

Download Commands Reference

A. Download Transcript

Primary: yt-dlp

yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt \
  -o "{vault_root}/transcripts/{youtube_id}" "https://www.youtube.com/watch?v={youtube_id}"

VTT Cleanup (required after yt-dlp download)

The .vtt file contains timestamps, cue position markers, and duplicate lines. Clean it before analysis:

  1. Strip all timestamp lines (lines matching \d{2}:\d{2}.*-->)
  2. Strip cue position markers (lines like align:start position:0%)
  3. Remove blank lines
  4. Deduplicate consecutive identical lines
  5. Save the cleaned text as {vault_root}/transcripts/{youtube_id}.txt

Fallback: youtube-transcript-api

Use if yt-dlp fails (e.g., no auto-captions available):

"{python_path}" -c "
from youtube_transcript_api import YouTubeTranscriptApi
transcript = YouTubeTranscriptApi.get_transcript('{youtube_id}', languages=['en'])
for entry in transcript:
    print(entry['text'])
" > "{vault_root}/transcripts/{youtube_id}.txt"

Where {python_path} is config.python_path from the tracking database.

This produces clean text directly — no VTT cleanup needed.

B. Download Slides PDF

"{python_path}" -m gdown "https://drive.google.com/uc?id={google_drive_id}" \
  -O "{vault_root}/slides/{google_drive_id}.pdf"

Where {python_path} is config.python_path from the tracking database.

Then read the PDF to understand slide content and visual structure.

Install with Tessl CLI

npx tessl i jbaruch/speaker-toolkit

skills

CHANGELOG.md

README.md

tile.json