YouTube Intelligence Extractor

Overview

This skill turns YouTube video transcripts into structured, actionable intelligence across four domains that matter to Chibitek Labs: productivity, AI prompting, platform/systems engineering, and creative building. Instead of passive consumption, every video becomes a reusable asset — extracted todos, prompts you can run immediately, frameworks you can apply, and insights mapped to your current work.

After presenting the report, always save it as a markdown file and present it to the user. This is standing behavior — do not wait to be asked.

Quick Reference

Step 1: Fetch the Transcript

Method A — Wave Tube (preferred — works from server/cloud environments)

Wave Tube mirrors YouTube transcripts and is not IP-blocked. Always try this first.

URL pattern: https://tube.wave.co/[video-slug]-[VIDEO_ID]

To find the slug: search "[VIDEO_ID] youtube" — the Wave Tube result will appear. Or web_search for the video title + video ID; Wave Tube typically appears in results.

Use the web_fetch tool on the Wave Tube URL. The full transcript appears in the page body under "## Transcript". Also captures: title, channel, views, duration, description, and timestamps in the same fetch.

Why Wave Tube first: youtube-transcript-api fails with IpBlocked when Claude runs on cloud infrastructure (AWS/GCP/Azure). Wave Tube is a reliable public mirror that bypasses this entirely and also provides video metadata in the same fetch.

Method B — Python (works locally on macOS, fails in server environments)

pip install youtube-transcript-api --break-system-packages -q

from youtube_transcript_api import YouTubeTranscriptApi
import re

def extract_video_id(url):
    patterns = [
        r'(?:v=|youtu\.be/)([a-zA-Z0-9_-]{11})',
        r'(?:embed/)([a-zA-Z0-9_-]{11})',
    ]
    for p in patterns:
        m = re.search(p, url)
        if m:
            return m.group(1)
    return url  # assume it's already a video ID

# IMPORTANT: Use instance method .fetch(), NOT .get_transcript()
# The API changed in v1.0+ — always instantiate first
video_id = extract_video_id("VIDEO_URL_HERE")
transcript = YouTubeTranscriptApi().fetch(video_id)
full_text = " ".join([entry.text for entry in transcript])
print(full_text)

Common mistake: YouTubeTranscriptApi.get_transcript() no longer exists in v1.0+. Always use YouTubeTranscriptApi().fetch(video_id) — instantiated, not class method.

Method C — Manual paste (always works, zero dependencies)

If both automated methods fail, ask the user to:

1. Go to the YouTube video
2. Click the ... menu → Show transcript
3. Copy all the transcript text
4. Paste it directly into the chat

Then proceed with the extraction pipeline on the pasted text.

Step 2: Run the Extraction Pipeline

Once you have the transcript text, extract across all four domains. Each domain is independent — process them cleanly and present them in sequence.

Domain 1 — Todos & Action Items

Extract concrete, doable actions mentioned or implied in the video.

Output format:

## ✅ Todos & Action Items
- [ ] [Specific action] — [context, tool, or condition if mentioned]

Rules:

• Every item must be specific enough to act on without watching the video
• Include tool names, URLs, timeframes where mentioned
• Do NOT include vague advice ("learn more about X") — skip it or make it concrete

Domain 2 — Advice & Frameworks

Extract principles, mental models, frameworks, and strategic advice that apply repeatedly — not just one-time instructions.

Output format:

## 💡 Advice & Frameworks

### [Framework Name]
**The idea:** ...
**Why it matters:** ...
**How to apply it:** ...

Domain 3 — AI Prompts (Extractable & Ready to Use)

Extract any AI prompts, prompt strategies, or prompt patterns mentioned, shown, or implied. Reconstruct them as runnable prompts — not descriptions of prompts.

Output format:

## 🤖 AI Prompts & Techniques

### [Technique Name]
**Purpose:** [What this achieves]
**Prompt:**

[Full, ready-to-run prompt text in a fenced code block]

**Notes:** [Model, context, or usage tips]

Rules:

• If a prompt is partially shown, reconstruct the most likely complete version
• Format prompts in fenced code blocks so they are easy to copy
• Never just describe a prompt — always write the actual text

Domain 4 — Platform, Engineering & Creative Insights

Extract anything relevant to systems design, platform architecture, tooling, development workflows, APIs, infrastructure patterns, or creative production.

Output format:

## ⚙️ Platform, Engineering & Creative Insights

### [Insight Name]
**Domain:** [Engineering / Platform / Creative / Tooling]
**Insight:** ...
**Application for Chibitek/UAMH:** [How this maps to UAMH, STIKI, Nexus, or OpenClaw]

Rules:

• Always include the Chibitek/UAMH application note — generic insights are less useful
• If an insight maps to multiple Chibitek systems, name them all

Step 3: Present the Intelligence Report

Report Header (always include)

# 🎬 Intelligence Report: [VIDEO TITLE]
**Channel:** [Channel Name]
**Published:** [Date] · [Duration] · [View count]
**Topic:** [2-3 word tag]
**Source:** [Full YouTube URL]
**Summary:** [One sentence: what this video teaches and who it's for]

---

Report Footer (always include)

## 🏆 Top 3 Immediate Actions
1. [Highest-leverage action from the report]
2. [Second most valuable]
3. [Third]

---
*Extracted by Chibitek Labs YouTube Intelligence Extractor · [Date]*

Step 4: Save the Report (Always — Do Not Wait to Be Asked)

After presenting the report in chat, immediately save it as a markdown file and present it using present_files. This is standing behavior.

File naming convention:

intel-[short-slug-from-title].md

Example: intel-openclaw-5-things.md

Save location: /mnt/user-data/outputs/ (or appropriate workspace output dir)

Every video intelligence report is a reusable asset. Saving automatically means the output is never lost when the chat session ends.

Step 5: Update This Skill After Every Improvement

After any run that reveals a better approach, edge case, or new pattern, update this SKILL.md immediately. Do not wait to be asked. This is standing behavior.

What counts as an improvement worth capturing:

• A fetch method that worked better than expected
• A new transcript source discovered
• An output format tweak that made the report cleaner
• A new anti-pattern encountered in the wild
• A domain or extraction type that proved more or less useful than expected

Common Mistakes (Anti-Patterns)

Trying Python fetch first in server environments The youtube-transcript-api library is always IP-blocked when Claude runs on cloud infrastructure. Go to Wave Tube first — it is faster and more reliable in this context.

Using the old class method API YouTubeTranscriptApi.get_transcript() throws AttributeError in v1.0+. Use YouTubeTranscriptApi().fetch(video_id) — instantiated instance, not class method.

Summarizing instead of extracting Do not write a paragraph about what the video covers. Extract discrete, usable pieces. A summary is what YouTube's description does. This skill does more.

Vague action items Bad: - [ ] Learn more about AI prompting Good: - [ ] Try reverse prompting: ask OpenClaw to extract your goals by asking you questions

Paraphrasing prompts instead of reconstructing them If a speaker reads a prompt aloud or shows it on screen, write the actual prompt text in a fenced code block — ready to copy and run. Never just describe it.

Skipping the Chibitek mapping in Domain 4 Generic engineering insights are half as useful as ones mapped to UAMH, STIKI, or Nexus. Always close the loop on how the insight applies to Chibitek's current work.

Not saving the report automatically The report is a reusable asset. Save it every time without being asked.

Not updating the skill after improvements If this run taught you something new, update the skill before ending the session.

Quality Checklist

Before closing out a run, verify:

• Transcript was fetched (Wave Tube preferred, Python fallback, manual last resort)
• Report header includes: title, channel, date, duration, views, source URL, summary
• All four domains are present (unless user requested a subset)
• Action items are specific and immediately actionable
• At least one AI prompt is reconstructed as a full, runnable prompt in a code block
• Every Domain 4 insight has a Chibitek/UAMH application note
• Top 3 immediate actions are surfaced in the footer
• Report was saved as a markdown file and presented via present_files
• Skill was updated if any improvements were discovered this run

Dependencies

Wave Tube (Method A) requires no installation — use the web_fetch tool directly.

pip install youtube-transcript-api --break-system-packages  # Method B only
pip install yt-dlp --break-system-packages                  # Optional fallback

Chibitek Context Notes

This skill is designed around Erick's four priority domains:

• Productivity: todos, systems, habits, workflows
• AI Prompting: extractable, runnable, copy-paste-ready prompts
• Platform Engineering: architecture, tooling, infra, APIs, agent design
• Creating: design, content, product, creative workflows

When insights span multiple domains, list them in all applicable sections. Always map Domain 4 insights back to UAMH, STIKI, Nexus, or OpenClaw specifically.

Changelog