Youtube Extract

youtube-extract — pull named artifacts from a YouTube video

You are the host agent running this skill. The user has asked for the resources, references, links, code, books, tools, or people mentioned in a video. Your job is to produce a categorized reference list at ~/youtube-reports/{date}-{slug}-{video_id}-extract.md.

This skill is purely factual reference extraction. It does not summarize, recommend, judge, or rank. Every entry is a verbatim mention from the transcript with a timestamp.

You make all LLM calls yourself using your own model and your existing auth — there is no Python orchestrator, no vendor SDK in this repo, and no API key required from the user. The only system requirement is Python 3.11+ with yt-dlp and youtube-transcript-api installed (Step 1.5 verifies this).

Workflow — follow these steps in order

Step 0 — Resolve skill paths

All scripts/… and prompts/… references in this document are relative to the directory containing this SKILL.md file — not the user's working directory. Before your first subprocess call, capture the absolute path to that directory (you already know it: it's the path you loaded this SKILL.md from). Use it as <SKILL_DIR> for every script and prompt path below.

In every shell call, pass quoted absolute paths:

python3 "<SKILL_DIR>/scripts/fetch.py" "<url>" --cache

Do not assume the user's working directory is the repo root. Do not rely on a .venv being activated.

Step 1 — Extract the video URL or 11-char ID from the user's input

Accepted forms (each is recognized by scripts/fetch.py):

• Plain 11-char ID: n0phBDPz8z0
• https://www.youtube.com/watch?v=… (also m.youtube.com, music.youtube.com)
• https://youtu.be/…
• https://www.youtube.com/shorts/…, /embed/…, /live/…

Reject playlist URLs (/playlist) — pass a specific video instead. If no URL is found in the user's message, ask them for one and stop.

Always pass the URL inside double quotes when shelling out — zsh and other shells will treat the ? in ?v=… as a glob otherwise.

Step 1.5 — Pre-flight dependency check

Run once, before the first fetch:

python3 "<SKILL_DIR>/scripts/doctor.py"

If it exits non-zero, surface the printed pip3 install command verbatim to the user (doctor.py tailors it to the user's Python — adding --break-system-packages for PEP 668 environments like Homebrew Python on macOS), ask them to run it, and stop.

Step 2 — Fetch transcript and metadata

python3 "<SKILL_DIR>/scripts/fetch.py" "<url-or-id>" --cache

Interpret the exit code per the standard mapping (0 success, 2 documented rejection, 1 unexpected error). Documented rejection codes (exit 2): INVALID_URL, PLAYLIST, LIVE_STREAM, TOO_SHORT (under 180s), NO_TRANSCRIPT, NON_ENGLISH. Surface rejections verbatim and stop — do not attempt the LLM passes.

Step 3 — Pass 1: Structure extraction (shared with verdict, summary, claims)

Cache file: ~/youtube-reports/.cache/{video_id}-pass1.json. Pass 1 is shared infrastructure — same prompt, same input, same output across all four skills. Likely a free cache hit if any sister skill ran on this video first.

1. Try a cache read in one subprocess call:

   echo '{"transcript": <full Step 2 fetch JSON>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 1 <video_id> "<SKILL_DIR>/prompts/extract_structure.md"
   

   Exit 0 = HIT (parse stdout as JSON). Exit 1 = MISS.

2. On MISS, read prompts/extract_structure.md, apply it as a single LLM pass, parse the model response as JSON, then write the cache wrapper:

   echo '{"inputs": {"transcript": <full fetch JSON>}, "output": <Pass 1 JSON>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" write 1 <video_id> "<SKILL_DIR>/prompts/extract_structure.md"
   

Tell the user: Pass 1: cache hit or Pass 1: ran (N sections extracted).

Step 4 — Pass 2: Per-section artifact extraction

Cache file: ~/youtube-reports/.cache/{video_id}-extract-pass2.json.

• Prompt: prompts/extract_artifacts.md.
• Canonical inputs: {"pass1": <full Pass 1 output>, "transcript": <full fetch.py output>}.

The Pass 1 timestamps (section.start, section.end) are authoritative — pass them straight to segments.py. Do not search the transcript to "verify" or "snap" boundaries.

1. Try a cache read:

   echo '{"pass1": <Pass 1>, "transcript": <fetch.py output>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 2 <video_id> "<SKILL_DIR>/prompts/extract_artifacts.md"
   

   Exit 0 = HIT. Exit 1 = MISS.

On a cache miss — per-section execution

For each section in Pass 1's sections[], in order:

1. Slice the transcript via subprocess:

   python3 "<SKILL_DIR>/scripts/segments.py" <video_id> <section.start> <section.end>
   

   Stdout is a compact JSON object containing only the segments in [start, end).

2. Apply prompts/extract_artifacts.md as a single LLM pass with these inputs:

   {
     "pass1": {"video_id": "<id>", "sections": [<just this one section>]},
     "transcript": <stdout from step 1, parsed as JSON>
   }
   

   The model returns {"video_id":"<id>","by_section":{"<this section's id>":{...}}}.

3. Merge that one by_section entry into a running merged dict. Drop the section's transcript slice from your context.

After all sections are processed, write the cache wrapper:

echo '{"inputs": {"pass1": <Pass 1>, "transcript": <fetch.py output>}, "output": <merged Pass 2>}' | \
  python3 "<SKILL_DIR>/scripts/cache.py" write 2 <video_id> "<SKILL_DIR>/prompts/extract_artifacts.md"

Tell the user

Pass 2: cache hit or Pass 2: ran (N artifacts found across L/C/B/T/P) (where L/C/B/T/P are counts for links/code/books/tools/people).

Step 5 — Pass 3: Synthesis into the extract report

Cache file: ~/youtube-reports/.cache/{video_id}-extract-pass3.json.

• Prompt: prompts/generate_extract.md.
• Canonical inputs: {"metadata": {title,channel,duration_seconds,view_count,upload_date}, "pass1": <Pass 1>, "pass2": <Pass 2>}.
• Pass 3 does not need the transcript at all. Pass 2 already contains every artifact's verbatim quote and timestamp. Do not Read ~/youtube-reports/.cache/{video_id}.json for this pass.

1. Try a cache read:

   echo '{"metadata": {...}, "pass1": <Pass 1>, "pass2": <Pass 2>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 3 <video_id> "<SKILL_DIR>/prompts/generate_extract.md"
   

   Exit 0 = HIT (stdout is the report markdown). Exit 1 = MISS.

2. On MISS, apply prompts/generate_extract.md as a single LLM pass. Strip the outer ``` fence; the inner text is the report. Then write the cache wrapper:

   echo '{"inputs": <same canonical inputs>, "output": "<stripped report markdown>"}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" write 3 <video_id> "<SKILL_DIR>/prompts/generate_extract.md"
   

Tell the user: Pass 3: cache hit or Pass 3: ran.

Step 6 — Write the final report

Filename: {date}-{slug}-{video_id}-extract.md (date = first 10 chars of fetched_at, slug = slug field, video_id = 11-char ID).

~/youtube-reports/{date}-{slug}-{video_id}-extract.md

Always overwrite if it exists. Do not print the full report inline — terminal output is the dashboard in Step 7.

Step 7 — Show the extract dashboard inline

Print this dashboard directly to the user. Borders are exactly 54 box-drawing characters ━. Two-space indent on every content line.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  📚 Extract  ·  {title_truncated}  ·  {duration_human}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  {N_total} artifacts extracted

  🔗 Links    {N_links}     💻 Code     {N_code}     📖 Books    {N_books}
  🛠  Tools    {N_tools}     👤 People   {N_people}

  Notable
     • {bullet 1, ≤ 70 chars}
     • {bullet 2, ≤ 70 chars}
     • {bullet 3, ≤ 70 chars}

  📄 ~/youtube-reports/{date}-{slug}-{video_id}-extract.md

Field extraction

• title_truncated: first 50 chars of title from Step 2 fetch JSON, suffixed with … if longer.
• duration_human: M:SS if duration_seconds < 3600, else H:MM:SS.
• N_total, N_links, N_code, N_books, N_tools, N_people: counts parsed from the Pass 3 report's OVERVIEW block (one line per category with its count).
• Notable bullets: the first three bullets from the Pass 3 report's NOTABLE section. Truncate each at 70 chars + … if longer. If fewer than 3 exist, show what's there. If Notable is empty, omit the entire Notable block (header and bullets) from the dashboard.
• File path footer: literal 📄 + the path written in Step 6.

If N_total == 0, render the dashboard with all-zero counts and the literal line No named artifacts found in this video. in place of the Notable block. The user gets a clear "nothing to extract" signal.

Cache protocol

Identical to youtube-verdict's cache protocol. See skills/youtube-verdict/SKILL.md → "Cache protocol — exact contract" for the full spec.

Always use cache.py read and cache.py write (Steps 3–5) — they handle wrapper construction, hashing, hit detection, and atomic writes. Inline shell or python3 -c snippets drift across host agents and produce spurious cache misses.

Skill-specific cache files:

Per-pass canonical inputs:

Cross-platform notes

• Steps 3, 4, and 5 use your own LLM and auth. No ANTHROPIC_API_KEY / OPENAI_API_KEY / vendor key required.
• Subprocess calls: doctor.py (Step 1.5), fetch.py (Step 2), cache.py read / cache.py write (Steps 3–5), segments.py (Step 4 inside per-section loop). If your host can't shell out, importing each module from <SKILL_DIR>/scripts/ is equivalent.
• All cache wrapper construction goes through cache.py write so per-host JSON quirks cannot produce a spurious miss.
• If python3 "<SKILL_DIR>/scripts/fetch.py" ever fails with ModuleNotFoundError, run python3 "<SKILL_DIR>/scripts/doctor.py" for the exact pip3 install command for the user's Python.

Output format reminder

• Pass 1 output: shared with verdict and summary, see prompts/extract_structure.md.
• Pass 2 output: JSON object {video_id, by_section} where each section has five arrays (links, code, books, tools, people), every entry with timestamp + verbatim quote — see prompts/extract_artifacts.md.
• Pass 3 output: a single fenced markdown block following the report layout in prompts/generate_extract.md. Tone is factual reference extraction — no recommendations, no rankings beyond the small Notable shortlist, no judgments about whether the resources are good.

Scope reminder

Audio-only transcripts cannot capture content that is only shown visually (e.g., on-screen code that is never spoken). The skill extracts what is audibly mentioned: function names spoken aloud, library imports dictated, command-line invocations read out, URLs spelled or named, book titles announced, tools referenced by name, people introduced. Visual-only artifacts are out of scope.