yt-whisper-skill

Transcribes YouTube videos with the local scripts/yt-transcribe.sh and a self-contained Whisper toolchain (its own .venv, created by scripts/install.sh). It can also turn a transcript (or any input document) into a structured AI summary and a compact research plan.

Features

• Download YouTube audio and transcribe it with Whisper on CUDA
• Auto-pick a faster model for long videos
• Respect t= / start= / end= timestamps in YouTube URLs
• Save to ~/Transcriptions/<Category>/ with a URL + title header
• Infer the category from the title, or force one with --category
• Support a single URL, several URLs, or bare 11-char video IDs
• Optional language detection or forced language
• Delete temporary media after transcription
• Produce a structured *_AISUMMARY.md report from a transcript
• Produce a compact *_AISEARCHPLAN.md research plan from ANY input file

Quick Start

# One-time setup (creates the local venv and installs yt-dlp + whisper)
bash {baseDir}/scripts/install.sh

# Transcribe a single video
{baseDir}/scripts/yt-transcribe.sh "https://youtu.be/VIDEO_ID"

# Several videos in one run
{baseDir}/scripts/yt-transcribe.sh "https://youtu.be/ID1" "https://youtu.be/ID2"

# Force a language, or force a category
{baseDir}/scripts/yt-transcribe.sh "https://youtu.be/VIDEO_ID" --language it
{baseDir}/scripts/yt-transcribe.sh "https://youtu.be/VIDEO_ID" --category Philosophy

# Live URL with a timestamp is normalized, the time range is preserved
{baseDir}/scripts/yt-transcribe.sh "https://www.youtube.com/live/VIDEO_ID?t=276s"

If install.sh has been run, the same script is also available on PATH as yt-transcribe.

Requirements

• ffmpeg on PATH
• the local venv at {baseDir}/.venv (created by scripts/install.sh; holds yt-dlp and openai-whisper)
• a JavaScript runtime for yt-dlp (node, deno, bun or quickjs)
  • YouTube requires one; the script auto-resolves it from PATH and the usual install locations (~/.local/bin, ~/.nvm/versions/node/*, ~/.deno/bin, /usr/bin, ...) and passes the concrete path as --js-runtimes <name>:<path>
  • symptom when missing: HTTP Error 403: Forbidden — this is NOT a YouTube block and NOT a yt-dlp bug, it means no JS runtime was found

Output Format

Each transcription is saved as ~/Transcriptions/<Category>/<safe_title>_<video_id>.txt and starts with:

URL: https://youtube.com/watch?v=...
Title: <Video Title>

[Full transcript content...]

Temporary media is deleted right after transcription.

Operating Rules

Hard rules for agents:

• MUST invoke exactly {baseDir}/scripts/yt-transcribe.sh (or the installed yt-transcribe)
• MUST pass each requested video as a quoted positional argument to that script
• MUST prefer the full YouTube URL when the user provides one
• MUST NOT strip a provided YouTube URL down to a bare video ID before execution
• youtube.com/live/<id>, youtube.com/shorts/<id>, youtu.be/<id>, and bare 11-char IDs are acceptable; the script normalizes them internally
• MUST preserve t=, start=, and end= query params when using the user URL
• MUST use timeout >= 7200 for normal full-video transcriptions unless the user explicitly wants a short clip
• MUST NOT use timeout: 300 for a normal transcription request
• If the user gives a live URL, pass it through directly; do not invent a partial command from just the ID
• If the user gives multiple URLs, pass them all in one command when feasible
• On failure with Unknown option, treat it as an invocation contract bug first, not a Whisper failure
• On failure with HTTP Error 403: Forbidden, suspect a missing JavaScript runtime before anything else: check the JS runtime: line the script prints, and verify with command -v node deno bun qjs
• Treat exitCode != 0 as failure, and also treat the run as failure if the output contains Error:, Video unavailable, Unknown option, or Audio download failed
• After a successful transcription, report the saved path first
• Do not claim success just because other transcript files already exist in ~/Transcriptions
• Never launch a second run for the same URL while another run for it is still in progress
• If --category is omitted, infer it from the title (falling back to Unsorted); use --category <Category> only to force a different bucket
• Do not read the whole transcript back into the LLM by default; read only a short excerpt if needed
• After a successful transcription, if the user asks for a summary/report, follow the ## Detailed AI Summary section and save ~/Transcriptions/.agent/summaries/<Category>/<same-name>_AISUMMARY.md
• When the user asks for a research plan / NotebookLM input, produce <same-name>_AISEARCHPLAN.md (for ANY input file, not only transcriptions) following the ### Research plan subsection, staying under ~4,600 characters
• MUST NOT write secrets into any repo file; never commit tokens (keep them in a secrets store outside the repo)

Runtime Notes

• The script logs the effective duration, model, CUDA device and the resolved JavaScript runtime before transcribing
• Long videos can legitimately take far longer than 6-10 minutes; tool timeouts must reflect video length
• Timestamped links like ...?t=276s are clipped requests, not always full-video transcriptions

Detailed AI Summary (*_AISUMMARY.md)

After a successful transcription, optionally produce a structured, implementation-oriented report of the video and archive it next to the transcript. Purpose: turn a raw transcript into a navigable knowledge artifact (macro-themes, sub-themes, fundamental concepts, quotes, actionable takeaways) that is usable for frontier implementation work.

Where to save

• Directory: ~/Transcriptions/.agent/summaries/<Category>/
  • <Category> MUST mirror the transcript's category subfolder. A transcript in ~/Transcriptions/Technology/ maps to ~/Transcriptions/.agent/summaries/Technology/.
  • If the transcript sits directly in ~/Transcriptions/ (no category), use Unsorted.
• Filename: same base name as the transcript with _AISUMMARY.md appended (DHH_..._NYFGCESmikA.txt -> DHH_..._NYFGCESmikA_AISUMMARY.md)
• Create the folder if missing (mkdir -p). The .agent/ tree is metadata, never mixed with the raw transcripts.

How to build the summary

1. Read the transcript ENTIRELY before writing (it can be thousands of lines). Do not sample.
2. Extract, in this order: a titled header block with source metadata (URL, title, transcript path, duration, generator, date); a TL;DR of roughly 10-12 points; an ordered map of macro-themes (table: theme / focus / why it matters); for each macro-theme the sub-themes, fundamental concepts, verbatim key quotes and implications for frontier implementation; a glossary of recurring fundamental concepts; an actionable playbook; experiments/benchmarks mentioned; tensions/risks with counter-arguments; open questions; a short method note.
3. Keep it evidence-bound: every claim must trace back to the transcript and quotes must be verbatim. Mark paraphrases/translations explicitly. Never invent content.
4. Language: write the report in the user's language (default Italian), keeping technical terms in English.

Rules

• Do NOT overwrite an existing _AISUMMARY.md without asking; if one exists, ask first or write a dated variant.
• Back up any file before overwriting it (cp -a file file.bak-YYYYMMDD-HHMMSS).
• The summary is a separate artifact: never modify or truncate the original .txt transcript.

Research plan (*_AISEARCHPLAN.md)

For ANY input document - a transcription, or any other file the user provides - you can produce a research plan: a compact, formal, deterministic brief meant to be pasted into NotebookLM (or a similar deep-research tool) so it runs research across every theme of the input. It is NOT a summary, and NOT a list of keywords: it is a set of instructions plus one context-anchored prompt per theme.

Where to save

• Transcriptions: ~/Transcriptions/.agent/summaries/<Category>/<same-name>_AISEARCHPLAN.md (same folder and base name as the companion _AISUMMARY.md when one exists).
• Any other input file: <input-dir>/.agent/<input-base-name>_AISEARCHPLAN.md (create the .agent folder next to the source file if missing).
• Never modify or replace the source file.

Size cap (NON NEGOTIABLE)

• NotebookLM rejects long inputs: keep the plan within the length of the companion report's TL;DR + macro-theme map, i.e. at most ~4,600 characters / ~1,150 tokens. Target 3,600-4,400.
• Verify the real size before saving (wc -c / .length). If over, cut words, never themes.

How to build it well (do NOT compress into a keyword list)

1. Header (2-3 lines): the source of the input, the input file path, and the goal of the research.
2. A "Deterministic instructions" block: state the analyst role and require that EVERY theme be delivered as a card with the SAME fixed fields, in the SAME order. Default six fields: (1) framing, 5-8 lines, what it is and why it matters; (2) state of the art with cited sources; (3) evidence for/against, separating facts from opinions; (4) contextual glossary, every technical term defined with an example; (5) risks, limits and unknowns; (6) three follow-up questions. Also state the language, the "no isolated technical terms" rule, the traceability rule, and the final deliverable shape (one document, themes in order, plus N priority research questions).
3. One section per theme, as ### Tn - <theme> followed by ONE short paragraph made of a sentence of CONTEXT plus the research objective. Anchor every technical term inside the sentence and define it inline. NEVER emit bare term lists, entity lists or isolated keywords.
4. Keep it compact but complete: one short paragraph per theme, no repetition, no omissions.

Rules

• Deterministic and evidence-bound: the same structure for every theme, so results are comparable.
• Do not overwrite an existing _AISEARCHPLAN.md without asking; back up any file before overwriting it.
• The plan is a separate artifact: the source file stays untouched.