微信扫一扫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
| 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.
---
## 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.
---
## 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.