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randysalars

analytics-learning

by randysalars

0🍴 0📅 2026年1月25日
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SKILL.md


name: Analytics Learning tier: 4 load_policy: conditional description: Process YouTube analytics to extract actionable insights version: 1.0.0 parent_skill: growth-learning

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

/learn-analytics session-name

Input Data

User provides from YouTube Studio:

MetricDescription
ViewsTotal view count
Watch TimeTotal hours watched
Average View DurationMean watch time
Retention %% of video watched
Likes / DislikesEngagement signals
CommentsComment count
SharesSocial shares
Subscribers GainedNew subscriptions
ImpressionsHow often shown
CTRClick-through rate

Analysis Process

1. Benchmark Comparison

Compare session metrics to portfolio averages:

MetricThis SessionAverageVerdict
Retention48%42%Above average
Like Ratio6.2%5.8%Slightly above
Comments2418Above average

2. Pattern Identification

Correlate session attributes with performance:

AttributeCorrelation
Topic: Healing+15% retention
Duration: 25 minOptimal
Voice: Neural2-HConsistent
Binaural: Theta+8% engagement

3. Insight Extraction

Generate specific, actionable findings:

- 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:

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

PatternMeaningAction
Steep initial dropPoor hook/introImprove pre-talk
Drop at 5-7 minInduction too slowTighten pacing
Steady through journeyGood engagementMaintain approach
Drop at integrationExit feels abruptSmooth emergence

Target Retention by Section

SectionTarget 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 RatioInterpretation
>10%Exceptional resonance
6-10%Strong positive response
4-6%Normal engagement
<4%Review content quality

Comment Analysis Signals

SignalMeaning
Emotional sharingDeep impact
QuestionsInterest but confusion
RequestsUnmet needs
CriticismQuality issues

Session Attribute Tracking

For each session, track:

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

LevelDefinition
high5+ sessions, consistent pattern
medium3-4 sessions, emerging pattern
low1-2 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

  • Skill: tier4-growth/feedback-integration/ (comment analysis)
  • Knowledge: knowledge/lessons_learned.yaml
  • Knowledge: knowledge/analytics_history/

スコア

総合スコア

50/100

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