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youtube-summary

Writing & research

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Updated 10-16 09:46

About this skill

youtube-summary — fast summary tool for YouTube videos

You are the host agent running this skill. The user has asked for a summary, TL;DR, or "what's in this video" without asking for a verdict. Your job is to produce a structured summary at ~/youtube-reports/{date}-{slug}-{video_id}-summary.md.

This skill never says "watch" or "skip" — that's youtube-verdict's job. If the user is asking for a verdict, hand off; otherwise produce a neutral factual summary.

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 (no matches found errors).

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. Do not retry the fetch until the user confirms the install succeeded.

Step 2 — Fetch transcript and metadata

Run as a subprocess (no LLM call):

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

The --cache flag reads/writes ~/youtube-reports/.cache/{video_id}.json so a second run on the same video skips the network entirely.

Interpret the exit code:

Exit Meaning Action
0 Success Parse stdout JSON; continue to Step 3
2 Documented rejection Parse stderr JSON {error, message, video_id}; surface verbatim to the user and stop
1 Unexpected error Surface to user and stop

Documented rejection codes (exit 2): INVALID_URL, PLAYLIST, LIVE_STREAM, TOO_SHORT (under 180s), NO_TRANSCRIPT, NON_ENGLISH. Do not attempt the LLM passes when fetch rejects.

Step 3 — Pass 1: Structure extraction (shared with youtube-verdict)

Cache file: ~/youtube-reports/.cache/{video_id}-pass1.json.

This pass is shared infrastructure — the same prompt (prompts/extract_structure.md) and the same input (the fetch.py JSON) produce the same output regardless of which skill is asking. If a previous youtube-verdict run already wrote this cache file, this skill will hit it for free, and vice versa.

  1. Try a cache read in one subprocess call (canonical inputs piped in via stdin):

    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 means cache HIT — stdout is the Pass 1 output JSON. Exit 1 means MISS (stderr says why).

  2. On a MISS, read prompts/extract_structure.md, apply it to the transcript JSON as a single LLM pass following the prompt's instructions exactly, parse the model response as JSON. Then write the cache wrapper:

    echo '{"inputs": {"transcript": <full Step 2 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 one short line: Pass 1: cache hit or Pass 1: ran (N sections extracted).

Step 4 — Pass 2: Per-section summarization

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

The cache wrapper schema is identical to Pass 1. What differs:

  • Prompt: prompts/summarize_sections.md.
  • Canonical inputs: {"pass1": <full Pass 1 output>, "transcript": <full fetch.py output>}.
  • The output field is the merged Pass 2 JSON object ({video_id, by_section}).

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/summarize_sections.md"
    Exit 0 = HIT (parse stdout JSON, skip to "Tell the user"). Exit 1 = MISS, run per-section processing.

On a cache miss — per-section execution

Loading the entire transcript into context burns ~17 K tokens for a 15-min video and ~120 K for a 75-min one. Process the transcript section by section instead.

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

  1. Slice the transcript via subprocess (no LLM call):

    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). Capture stdout. Do not also Read the full transcript file.

  2. Apply prompts/summarize_sections.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 a JSON object {"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 before moving to the next section.

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/summarize_sections.md"

Tell the user

Pass 2: cache hit or Pass 2: ran (N sections summarized).

Step 5 — Pass 3: Synthesis into TL;DR report

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

Differences:

  • Prompt: prompts/generate_summary.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 section's summary and key points. 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_summary.md"

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

  2. On MISS, read prompts/generate_summary.md, apply it to the canonical inputs as a single LLM pass. The model's response is markdown wrapped in a single fenced code block; strip the outer ``` fence — the inner text is the report. Then write the cache wrapper with the stripped report as a JSON string:

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

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

Step 6 — Write the final report

Build the filename from the Step 2 fetch JSON:

  • {date} — first 10 characters of fetched_at (UTC, YYYY-MM-DD).
  • {slug} — the slug field from the fetch JSON (deterministic, lowercase, ≤ 60 chars; falls back to untitled).
  • {video_id} — the 11-char ID.

Write the unwrapped Pass 3 report (the markdown text from the cache output) to:

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

The -summary suffix differentiates this from youtube-verdict's output for the same video. Always overwrite if it exists. Do not print the full report inline — terminal output is the dashboard in Step 7.

Step 7 — Show the TL;DR dashboard inline

Print this dashboard directly to the user. Borders are exactly 54 box-drawing characters ━. Two-space indent on every content line. Soft-wrap the TL;DR paragraph around column 60.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  📝 TL;DR  ·  {title_truncated}  ·  {duration_human}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  {tldr_paragraph — 3–4 sentences, soft-wrapped to ~60 cols}

  🎯 Top takeaways
     • {bullet 1, ≤ 80 chars}
     • {bullet 2, ≤ 80 chars}
     • {bullet 3, ≤ 80 chars}

  ⏭️  Skippable     {comma-separated [start–end] ranges, or "—"}

  📄 ~/youtube-reports/{date}-{slug}-{video_id}-summary.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.
  • tldr_paragraph: the prose paragraph(s) under the TL;DR header in the Pass 3 report. Strip leading/trailing whitespace. Soft-wrap to ~60 cols at word boundaries; indent wrapped lines with 2 spaces (align under the dashboard's content indent).
  • Top takeaways: the first three bullets from the KEY TAKEAWAYS section of the Pass 3 report. Truncate each bullet at 80 chars + … if longer. If fewer than 3 bullets exist, show what's there.
  • Skippable: parse the SKIPPABLE SECTIONS section. Show comma-separated [start–end] ranges. If the section is empty or missing, render the value as —.
  • File path footer: literal 📄 + the path written in Step 6.

Cache protocol

Identical to youtube-verdict's cache protocol. See skills/youtube-verdict/SKILL.md → "Cache protocol — exact contract" for the full spec (file layout, wrapper schema, hashing recipe, hit decision, invalidation events).

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:

Filename Owner Contents
{video_id}.json scripts/fetch.py Transcript JSON (shared)
{video_id}-pass1.json shared (verdict + summary) Pass 1 cache wrapper
{video_id}-summary-pass2.json this skill Pass 2 cache wrapper
{video_id}-summary-pass3.json this skill Pass 3 cache wrapper

Per-pass canonical inputs:

Pass Prompt Canonical inputs
1 prompts/extract_structure.md {"transcript": <full fetch.py JSON>}
2 prompts/summarize_sections.md {"pass1": <Pass 1 output>, "transcript": <full fetch.py JSON>}
3 prompts/generate_summary.md {"metadata": {title,channel,duration_seconds,view_count,upload_date}, "pass1": <Pass 1>, "pass2": <Pass 2>}

Cross-platform notes

  • Steps 3, 4, and 5 use your own LLM and auth. No ANTHROPIC_API_KEY / OPENAI_API_KEY / vendor key required.
  • The subprocess calls are: doctor.py (Step 1.5), fetch.py (Step 2), cache.py read / cache.py write (Steps 3–5), segments.py (Step 4 inside the 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 on the next run.
  • 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, see prompts/extract_structure.md.
  • Pass 2 output: JSON object {video_id, by_section} where each section has summary (2–4 sentences) and key_points (array of short strings) — see prompts/summarize_sections.md.
  • Pass 3 output: a single fenced markdown block following the report layout in prompts/generate_summary.md. Tone is factual and neutral — no recommendations, no judgments, no "you should watch this". The output answers "what was actually said?" and nothing more.