Video Intelligence

Video Intelligence Extraction

Overview

Extract actionable intelligence from video content across YouTube, Vimeo, and company websites. Video reveals what text sources cannot: facility scale, team composition, product demonstrations, signage, event attendance, and operational reality.

This skill focuses on metadata + caption extraction (fast, free, no storage cost) and only downloads actual video files when visual analysis is required.

When to Use

• Target company has a YouTube channel (even a small one — Shorts count)
• Founder or leadership appears in podcast/interview videos
• Product demos or walkthroughs exist on any platform
• Event recordings show the company's booth, presentations, or attendance
• You need to verify claims (facility size, team count, product range) with visual evidence
• Company website has embedded video content

Prerequisites

Phase 1: Discovery

YouTube Channel Enumeration

# List ALL videos on a channel (returns video IDs + titles, no download)
yt-dlp --flat-playlist --print "%(id)s %(title)s" "https://www.youtube.com/@CHANNEL_HANDLE"

# Include upload dates for timeline analysis
yt-dlp --flat-playlist --print "%(upload_date)s %(id)s %(title)s" "https://www.youtube.com/@CHANNEL_HANDLE"

YouTube Search

# Search YouTube (top 10 results)
yt-dlp --flat-playlist --print "%(id)s %(title)s" "ytsearch10:COMPANY_NAME"

# Broader search (top 30)
yt-dlp --flat-playlist --print "%(id)s %(title)s" "ytsearch30:COMPANY_NAME founder interview"

Other Discovery Methods

Discovery Checklist

• Target company's own YouTube channel
• Founder/CEO personal channel or guest appearances
• Industry podcast channels where target was interviewed
• Event/conference channels with target's presentations
• Partner/supplier channels mentioning target
• Local news/media channels covering target

Phase 2: Capture

Metadata + Captions (Default — No Video Download)

# Entire channel — metadata + auto-captions, no video files
yt-dlp --write-info-json --write-auto-sub --sub-lang en --skip-download \
  --sleep-interval 2 "https://www.youtube.com/@CHANNEL"

# Single video
yt-dlp --write-info-json --write-auto-sub --sub-lang en --skip-download \
  "https://www.youtube.com/watch?v=VIDEO_ID"

# Multiple specific videos from a list
yt-dlp --write-info-json --write-auto-sub --sub-lang en --skip-download \
  --sleep-interval 2 -a video_urls.txt

Key yt-dlp Flags

Download Video Files (When Visual Analysis Needed)

# 720p max (good balance of quality vs. file size)
yt-dlp -f "bestvideo[height<=720]+bestaudio" --merge-output-format mp4 VIDEO_URL

# Audio only for Whisper transcription
yt-dlp -x --audio-format mp3 VIDEO_URL

Output File Structure

project/
├── raw/video/
│   ├── Title of Video [VIDEO_ID].info.json    # Metadata
│   ├── Title of Video [VIDEO_ID].en.vtt       # Auto-captions (VTT format)
│   └── Title of Video [VIDEO_ID].mp4          # Video file (only if downloaded)
├── transcripts/
│   ├── VIDEO_ID_transcript.txt                # Parsed plain text
│   └── VIDEO_ID_transcript.txt
└── analysis/
    └── video_intelligence_summary.md

Phase 3: VTT Parsing

Convert VTT Captions to Plain Text

import re
import glob

def vtt_to_text(vtt_path):
    """Parse VTT subtitle file to clean plain text."""
    with open(vtt_path) as f:
        content = f.read()
    # Remove WEBVTT header and metadata
    content = re.sub(r'WEBVTT.*?\n', '', content, flags=re.DOTALL)
    # Remove timestamp lines
    content = re.sub(r'\d{2}:\d{2}:\d{2}\.\d+ --> .*', '', content)
    # Remove HTML-style formatting tags
    content = re.sub(r'<[^>]+>', '', content)
    # Deduplicate consecutive repeated lines and join
    lines = []
    for l in content.split('\n'):
        l = l.strip()
        if l and not l.isdigit() and (not lines or l != lines[-1]):
            lines.append(l)
    return ' '.join(lines)


def batch_parse_vtts(vtt_dir, output_dir):
    """Parse all VTT files in a directory to plain text."""
    import os
    os.makedirs(output_dir, exist_ok=True)
    for vtt_file in glob.glob(f"{vtt_dir}/*.vtt"):
        text = vtt_to_text(vtt_file)
        # Extract video ID from filename pattern "Title [VIDEO_ID].en.vtt"
        video_id = re.search(r'\[([^\]]+)\]', vtt_file)
        if video_id:
            out_name = f"{video_id.group(1)}_transcript.txt"
        else:
            out_name = os.path.basename(vtt_file).replace('.en.vtt', '_transcript.txt')
        with open(os.path.join(output_dir, out_name), 'w') as f:
            f.write(text)
        print(f"Parsed: {out_name} ({len(text.split())} words)")

Phase 4: Whisper Fallback (No Captions Available)

When auto-captions don't exist (common for Shorts, older videos, non-English content):

# Download audio only
yt-dlp -x --audio-format mp3 -o "%(id)s.%(ext)s" VIDEO_URL

# Transcribe with Whisper
whisper VIDEO_ID.mp3 --model base --language en --output_format txt

# For better proper noun accuracy (3-5x slower):
whisper VIDEO_ID.mp3 --model medium --language en --output_format txt

When Captions Typically Don't Exist

• YouTube Shorts (< 60 seconds)
• Videos uploaded before ~2015 (pre-auto-caption era)
• Unlisted/private videos
• Music-heavy content with minimal speech
• Non-English content without specified language

Phase 5: Intelligence Extraction

Metadata Intelligence (from info.json)

Every .info.json file contains:

Transcript Intelligence Checklist

• Named people (employees, partners, clients)
• Revenue/growth figures mentioned casually in interviews
• Future plans or strategy discussed
• Pain points or challenges admitted
• Competitor mentions (positive or negative)
• Hiring plans or team size references
• Product roadmap or feature announcements
• Customer names or case studies mentioned verbally

Visual Intelligence Checklist (Requires Video Download)

• Facility/warehouse size and condition
• Number of employees visible (team size proxy)
• Products on shelves or in demos (inventory breadth)
• Equipment and machinery (capital investment signals)
• Signage, branding, and logo usage
• Event booth size and design quality
• Vehicle fleet (delivery capability)
• Customer interactions caught on camera
• Geographic/location identifiers in background

Edge Cases and Gotchas

1. YouTube rate limiting — Use --sleep-interval 2 between downloads. DNS errors appear after 30+ rapid requests. For large channels, break into batches of 20.

2. Channel download filenames — yt-dlp names files as "Title [VIDEO_ID].ext" when downloading from channels. Spaces and special characters in titles break shell scripts. Use -o "%(id)s.%(ext)s" for clean filenames.

3. Duplicate uploads — Same interview uploaded under different channels or titles. Cross-reference by duration + upload_date to catch duplicates before spending time analyzing both.

4. LinkedIn native videos — CANNOT be downloaded programmatically. LinkedIn CDN URLs are authenticated and expire within hours. Flag as manual task: "Save as Web Complete" in browser, or screenshot key frames.

5. Shorts vs. full videos — Shorts URL format is youtube.com/shorts/ID but yt-dlp handles both formats transparently. Don't skip Shorts — they often show products, facilities, and team culture.

6. Unlisted videos — Not in channel listings but accessible via direct URL. Search engines may have indexed them. Check Google cache and Wayback Machine for unlisted URLs that were once linked publicly.

7. Auto-caption quality — No punctuation, sometimes garbled proper nouns (Celina → Selena, BROGAV → ProGraph). 95%+ word accuracy for standard English speech. Always verify company/person names against known-good sources.

8. Upload date vs. content date — Videos may be uploaded weeks or months after recording. Check description and audio cues for actual recording date. Event videos especially lag.

9. info.json is gold — Always capture with --write-info-json. The description field often contains timestamps, guest names, links to resources, and partner mentions that aren't in the video itself.

10. VTT timestamps are valuable — The plain-text conversion loses temporal information. Keep original VTT files as source of truth. Timestamps let you find specific moments for visual verification.

Anti-Patterns

Decision Tree

Content is on YouTube?
├── YES → yt-dlp --write-info-json --write-auto-sub --skip-download
│   ├── Has captions? → Parse VTT to text → Extract intelligence
│   └── No captions?
│       ├── Video > 60s with speech? → yt-dlp -x → Whisper → Analyze
│       └── Short/visual only? → Download video → Visual analysis
└── NO →
    ├── Vimeo → yt-dlp supports it (same workflow as YouTube)
    ├── LinkedIn video → Manual "Save as Web Complete" (flag as manual task)
    ├── Company website embed → Extract iframe URL → usually YouTube/Vimeo
    └── Other platform → Check yt-dlp supported sites list

Real-World Results (BROGAV Case Study)

Integration with Other Skills