Analytics Learning
Process YouTube analytics to extract actionable insights
Best use case
Analytics Learning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process YouTube analytics to extract actionable insights
Teams using Analytics Learning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analytics-learning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Analytics Learning Compares
| Feature / Agent | Analytics Learning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process YouTube analytics to extract actionable insights
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Analytics Learning Skill
> **Data-Driven Improvement**
This skill processes YouTube Studio analytics to understand what works and improve future sessions.
---
## Purpose
Extract actionable insights from performance data and update the knowledge base.
---
## Command
```bash
/learn-analytics session-name
```
---
## Input Data
User provides from YouTube Studio:
| Metric | Description |
|--------|-------------|
| Views | Total view count |
| Watch Time | Total hours watched |
| Average View Duration | Mean watch time |
| Retention % | % of video watched |
| Likes / Dislikes | Engagement signals |
| Comments | Comment count |
| Shares | Social shares |
| Subscribers Gained | New subscriptions |
| Impressions | How often shown |
| CTR | Click-through rate |
---
## Analysis Process
### 1. Benchmark Comparison
Compare session metrics to portfolio averages:
| Metric | This Session | Average | Verdict |
|--------|--------------|---------|---------|
| Retention | 48% | 42% | Above average |
| Like Ratio | 6.2% | 5.8% | Slightly above |
| Comments | 24 | 18 | Above average |
### 2. Pattern Identification
Correlate session attributes with performance:
| Attribute | Correlation |
|-----------|-------------|
| Topic: Healing | +15% retention |
| Duration: 25 min | Optimal |
| Voice: Neural2-H | Consistent |
| Binaural: Theta | +8% engagement |
### 3. Insight Extraction
Generate specific, actionable findings:
```yaml
- finding: "Healing topics achieve higher retention"
evidence: "62% vs 45% average across 5 sessions"
action: "Prioritize healing themes"
confidence: high
timestamp: "2025-01-15"
```
### 4. Knowledge Update
Store in `knowledge/lessons_learned.yaml`:
```yaml
lessons:
- id: "LESSON-2025-001"
category: "content"
finding: "Healing topics achieve higher retention"
evidence: "62% vs 45% average across 5 sessions"
action: "Prioritize healing themes"
confidence: high
sessions_analyzed:
- "inner-child-healing"
- "heart-chakra-restore"
- "grief-release-theta"
date_discovered: "2025-01-15"
date_validated: null
```
---
## Retention Analysis
### Retention Curve Patterns
| Pattern | Meaning | Action |
|---------|---------|--------|
| Steep initial drop | Poor hook/intro | Improve pre-talk |
| Drop at 5-7 min | Induction too slow | Tighten pacing |
| Steady through journey | Good engagement | Maintain approach |
| Drop at integration | Exit feels abrupt | Smooth emergence |
### Target Retention by Section
| Section | Target Retention |
|---------|------------------|
| Pre-Talk (0-3 min) | 90%+ |
| Induction (3-8 min) | 75%+ |
| Journey (8-22 min) | 55%+ |
| Integration (22-28 min) | 45%+ |
| Close (28-30 min) | 40%+ |
---
## Engagement Analysis
### Like Ratio Interpretation
| Like Ratio | Interpretation |
|------------|----------------|
| >10% | Exceptional resonance |
| 6-10% | Strong positive response |
| 4-6% | Normal engagement |
| <4% | Review content quality |
### Comment Analysis Signals
| Signal | Meaning |
|--------|---------|
| Emotional sharing | Deep impact |
| Questions | Interest but confusion |
| Requests | Unmet needs |
| Criticism | Quality issues |
---
## Session Attribute Tracking
For each session, track:
```yaml
session_attributes:
topic: "healing"
sub_topic: "inner_child"
duration: 25
depth_level: "Layer2"
voice_id: "en-US-Neural2-H"
binaural_target: "theta"
archetypes:
- "Guide"
- "Healer"
imagery_style: "eden_garden"
metrics:
views: 1250
watch_time_hours: 312
avg_view_duration: "14:58"
retention_percent: 48
likes: 78
dislikes: 2
comments: 24
shares: 12
subs_gained: 15
impressions: 8500
ctr: 14.7
```
---
## Confidence Levels
| Level | Definition |
|-------|------------|
| `high` | 5+ sessions, consistent pattern |
| `medium` | 3-4 sessions, emerging pattern |
| `low` | 1-2 sessions, hypothesis only |
---
## Output
After analysis:
1. **Summary Report**: Key findings with evidence
2. **Knowledge Update**: New entries in `lessons_learned.yaml`
3. **Recommendations**: Actions for next sessions
4. **Questions**: Areas needing more data
---
## Related Resources
- **Skill**: `tier4-growth/feedback-integration/` (comment analysis)
- **Knowledge**: `knowledge/lessons_learned.yaml`
- **Knowledge**: `knowledge/analytics_history/`Related Skills
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