Video Delivery Coach

Get better at video, video by video. This skill analyzes your recordings before you publish, identifying areas for improvement.

WHAT IT DOES

Analysis Type Metrics Tool Used

Voice Speech rate (WPM), pitch variation, volume consistency Librosa + Whisper

Facial Emotion timeline, eye contact frequency, smile frequency OpenCV + DeepFace + Mediapipe

Content Transcription, filler words, structure Faster-Whisper + Claude

Overall 5-dimension score (1-5 each, max 25) Claude analysis

SCORING RUBRIC

Dimension Score 1 Score 5

Content & Organization Disorganized, unclear Logical, well-structured

Delivery & Vocal Quality Monotone, many fillers Clear, varied, engaging

Body Language & Eye Contact No eye contact, stiff Direct gaze, natural movement

Audience Engagement Boring, loses attention Captivating, maintains interest

Language & Clarity Grammar issues, unclear Clear, impactful, professional

Total Score Interpretation:

• 5-9: Needs significant improvement

• 10-14: Developing skills

• 15-18: Competent speaker

• 19-22: Proficient speaker

• 23-25: Outstanding speaker

TRIGGERS

Use this skill when you say:

• "Analyze my video recording"

• "How was my delivery?"

• "Review my video before upload"

• "Check my presentation"

• "Coach my speaking"

USAGE

In Claude Code (Recommended)

"Analyze my video at /path/to/recording.mp4"

"Coach my delivery on the latest YouTube recording"

"What can I improve in this video?"

CLI Mode

Basic analysis

python scripts/analyze_video.py --video "/path/to/video.mp4"

Full analysis with all features

python scripts/analyze_video.py --video "/path/to/video.mp4" --full

Voice only (faster)

python scripts/analyze_video.py --video "/path/to/video.mp4" --voice-only

Save report

python scripts/analyze_video.py --video "/path/to/video.mp4" --output ~/reports/

OUTPUT FORMAT

Quick Summary

┌────────────────────────────────────────┐ │ VIDEO DELIVERY ANALYSIS │ │ recording_2025_01_15.mp4 │ ├────────────────────────────────────────┤ │ OVERALL SCORE: 18/25 (Competent) │ │ │ │ Content & Organization: 4/5 │ │ Delivery & Vocal Quality: 3/5 │ │ Body Language & Eye Contact: 4/5 │ │ Audience Engagement: 4/5 │ │ Language & Clarity: 3/5 │ └────────────────────────────────────────┘

Detailed Report

Video Delivery Analysis

File: recording_2025_01_15.mp4 Duration: 12:34 Date: 2025-01-15

VOICE ANALYSIS

Filler Words Detected:

• "um" - 8 times
• "you know" - 5 times
• "basically" - 3 times

Recommendation: Reduce "um" usage. Try pausing instead.

FACIAL ANALYSIS

Emotion Timeline:

• 0:00-2:00: Neutral (intro)
• 2:00-8:00: Happy/Engaged (main content)
• 8:00-10:00: Serious (data presentation)
• 10:00-12:34: Happy (conclusion)

Recommendation: More smiles during technical sections.

CONTENT ANALYSIS

Strengths:

• Clear opening hook
• Good use of clinical examples
• Strong call-to-action

Areas for Improvement:

• Could use more pauses after key points
• Consider adding more Hinglish transitions
• Section on side effects could be more structured

OVERALL FEEDBACK

What You Did Well:

1. Excellent pace - not too fast, not too slow
2. Good eye contact with camera
3. Clinical examples were relatable

What to Improve:

1. Reduce filler words (especially "um")
2. Add more smiles during technical explanations
3. Pause after key statistics for emphasis

Score: 18/25 - Competent Speaker You're delivering solid content with room for refinement.

HINGLISH-SPECIFIC ANALYSIS

This skill is calibrated for Hinglish content:

Feature What It Checks

Code-switching Natural Hindi ↔ English transitions

Pace adjustment Slower for English technical terms

Cultural markers Use of "ji", "beta", "aapko bata doon"

Engagement phrases "Dekho", "Suniye", "Samjhe?"

COMPARING OVER TIME

Track your improvement across recordings:

┌─────────────────────────────────────────────────────┐ │ PROGRESS TRACKER (Last 5 Videos) │ ├─────────────────────────────────────────────────────┤ │ Video │ Score │ Main Improvement │ │ ───────────────────────────────────────────────── │ │ Jan 10 │ 15/25 │ Baseline │ │ Jan 15 │ 18/25 │ Better eye contact │ │ Jan 20 │ 17/25 │ Fewer filler words │ │ Jan 25 │ 19/25 │ More varied pace │ │ Jan 30 │ 21/25 │ Natural Hinglish flow │ └─────────────────────────────────────────────────────┘

INTEGRATION

With Your Workflow

Record Video → Analyze with video-delivery-coach → Fix issues → Re-record (optional) → Publish

Feeds Into:

• youtube-script-master

• Script adjustments based on delivery feedback

• Personal improvement tracking

DEPENDENCIES

Core (required)

pip install anthropic python-dotenv rich

Voice analysis

pip install librosa moviepy faster-whisper

Facial analysis (optional - for full analysis)

pip install opencv-python mediapipe deepface tf-keras

Note: tf-keras is heavy (~500MB). Skip for voice-only mode.

API KEYS NEEDED

Key Purpose Status

ANTHROPIC_API_KEY Final analysis and coaching Already have

MODES

Voice-Only Mode (Lightweight)

python scripts/analyze_video.py --video file.mp4 --voice-only

• Requires: librosa, moviepy, faster-whisper

• Analyzes: Speech rate, pitch, volume, transcription, filler words

• Skip: Facial analysis (faster, lighter)

Full Mode (Comprehensive)

python scripts/analyze_video.py --video file.mp4 --full

• Requires: All dependencies including OpenCV, DeepFace, Mediapipe

• Analyzes: Everything including facial expressions

• Slower but complete

HOW CLAUDE SHOULD USE THIS SKILL

When user asks to analyze a video:

Step 1: Check if video file exists

import os if not os.path.exists(video_path): print("Video file not found") return

Step 2: Run analysis

python scripts/analyze_video.py --video "/path/to/video.mp4"

Step 3: Present results

• Show quick summary first

• Offer detailed breakdown if requested

• Provide actionable recommendations

Step 4: Track progress

• Compare with previous analyses

• Note improvements

• Identify persistent issues

SAMPLE OUTPUT

=== VIDEO DELIVERY ANALYSIS === File: hinglish_statin_video.mp4 Duration: 15:23

VOICE METRICS: ├── Speech Rate: 138 WPM (Target: 120-160) ✅ ├── Pitch Variation: 38.5 Hz ✅ Natural variation └── Volume: Consistent ✅

FILLER WORDS: ├── "um": 12 occurrences ├── "basically": 8 occurrences └── "you know": 5 occurrences

FACIAL METRICS: ├── Eye Contact: 68% ✅ Good ├── Smiles: 28% ⚠️ Below target (40%) └── Dominant Emotion: Engaged

CONTENT SCORE: ├── Content & Organization: 4/5 ├── Delivery & Vocal Quality: 3/5 ├── Body Language: 4/5 ├── Engagement: 4/5 └── Language & Clarity: 4/5

TOTAL: 19/25 (Proficient Speaker)

TOP 3 IMPROVEMENTS:

1. Replace "um" with pauses
2. Smile more during technical explanations
3. Slow down slightly when explaining statistics

HINGLISH NOTES: ✅ Natural code-switching ✅ Good use of "aapko batata hoon" ⚠️ Consider more "samjhe?" checks for engagement

NOTES

• Privacy: All analysis is local, video never uploaded anywhere

• Speed: Voice-only takes ~1 min, full analysis takes ~3-5 min

• File types: Supports MP4, MOV, AVI, MKV

• Duration: Works best with 5-30 minute videos

This skill helps you improve your delivery over time - not by judging, but by giving you objective data to work with.