Agent skill

Analytics Learning

Process YouTube analytics to extract actionable insights

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/analytics-learning

SKILL.md

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/

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