Video Delivery Coach

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

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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

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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

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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"

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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/

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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

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## VOICE ANALYSIS

| Metric | Value | Target | Assessment |
|--------|-------|--------|------------|
| Speech Rate | 145 WPM | 120-160 | ✅ Good |
| Pitch Variation | 42.3 Hz | >30 Hz | ✅ Engaging |
| Volume Consistency | 0.08 | <0.15 | ✅ Steady |

**Filler Words Detected:**
- "um" - 8 times
- "you know" - 5 times
- "basically" - 3 times

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

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## FACIAL ANALYSIS

| Metric | Value | Assessment |
|--------|-------|------------|
| Eye Contact Frequency | 72% | ✅ Good |
| Smile Frequency | 35% | ⚠️ Could increase |

**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.

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## 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

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## 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.

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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?"

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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    │
└─────────────────────────────────────────────────────┘

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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

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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.

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API KEYS NEEDED

Key	Purpose	Status
ANTHROPIC_API_KEY	Final analysis and coaching	Already have

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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

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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

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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

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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

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