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npx claudepluginhub joshuarweaver/cascade-content-creation-misc-1 --plugin nguyenthienthanh-piggytalesHow this agent operates — its isolation, permissions, and tool access model
Agent reference
piggytales:agents/automation/analytics-trackerThe summary Claude sees when deciding whether to delegate to this agent
**Agent ID:** analytics-tracker **Priority:** 75 **Phase:** Post-Publish **Category:** Automation **Required:** For performance monitoring --- Track and analyze published content performance across platforms. Provides insights for improving future content and validates success predictions. --- - Fetch real-time metrics from platforms - Track views, likes, comments, shares - Calculate engagement...
Publisher and performance tracker that manages content lifecycle and feedback loop
Read-only analytics agent that pulls social media post and account metrics via Ayrshare and summarizes performance. Delegated via @insights-analyst for safe stakeholder access.
Post-launch analytics agent that integrates tools like Google Analytics/Mixpanel/Amplitude, analyzes user engagement/funnels/retention, and delivers data-driven product optimization strategies.
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Agent ID: analytics-tracker Priority: 75 Phase: Post-Publish Category: Automation Required: For performance monitoring
Track and analyze published content performance across platforms. Provides insights for improving future content and validates success predictions.
youtube:
views:
fetch_frequency: hourly (first 24h), daily (7d), weekly
source: YouTube Analytics API
watch_time:
metric: minutes watched
avg_view_duration: calculated
engagement:
likes: count
dislikes: count (if available)
comments: count
shares: count
subscribers:
gained: from this video
lost: from this video
traffic_sources:
search: percentage
suggested: percentage
external: percentage
direct: percentage
tiktok:
views: count
likes: count
comments: count
shares: count
saves: count
watch_time:
avg_watch_time: seconds
completion_rate: percentage
profile_views: from this video
follows: from this video
engagement_rate:
formula: (likes + comments + shares) / views * 100
benchmark:
excellent: ">10%"
good: "5-10%"
average: "2-5%"
poor: "<2%"
viral_velocity:
formula: views_per_hour in first 24h
indicators:
viral: ">1000/hour sustained"
trending: ">100/hour"
normal: "<100/hour"
retention_score:
formula: avg_view_duration / video_duration * 100
benchmark:
excellent: ">70%"
good: "50-70%"
needs_work: "<50%"
schedule:
first_hour:
frequency: every 15 minutes
purpose: detect early viral signals
hours_1_to_24:
frequency: hourly
purpose: track launch performance
days_2_to_7:
frequency: every 6 hours
purpose: monitor growth curve
days_8_to_30:
frequency: daily
purpose: track long-term performance
after_30_days:
frequency: weekly
purpose: archive and summary
triggers:
milestone_check:
- 100 views
- 1000 views
- 10000 views
- 100000 views
engagement_spike:
- comments > 10/hour
- shares > 5/hour
def assess_performance(publication):
metrics = fetch_latest_metrics(publication)
predictions = get_predictions(publication.project_id)
assessment = {
'actual_vs_predicted': {
'views': compare(metrics.views, predictions.views_estimate),
'engagement': compare(metrics.engagement_rate, predictions.engagement_estimate),
},
'performance_tier': calculate_tier(metrics),
'viral_potential': assess_viral_signals(metrics),
'recommendations': generate_recommendations(metrics, predictions),
}
return assessment
def calculate_tier(metrics):
score = (
metrics.view_velocity_score * 0.3 +
metrics.engagement_rate_score * 0.3 +
metrics.retention_score * 0.2 +
metrics.growth_trend_score * 0.2
)
if score >= 90: return 'exceptional'
if score >= 75: return 'above_average'
if score >= 50: return 'average'
return 'below_average'
viral_signals:
early_indicators: # First 1-6 hours
- view_velocity > 500/hour
- engagement_rate > 15%
- share_rate > 5%
- comment_velocity > 20/hour
confirmation: # 6-24 hours
- sustained view growth
- increasing engagement rate
- appearing in trending
- external traffic spike
actions_on_viral:
- notify_user: immediate
- increase_fetch_frequency
- prepare_follow_up_content_suggestions
- document_success_factors
daily_summary:
generated_at: time
period: last 24 hours
overview:
total_views: number
total_engagement: number
best_performer: publication_id
worst_performer: publication_id
by_platform:
youtube:
publications: count
total_views: number
avg_engagement_rate: percentage
tiktok:
publications: count
total_views: number
avg_engagement_rate: percentage
highlights:
milestones_reached: Event[]
viral_content: Publication[]
needs_attention: Publication[]
comparison:
vs_yesterday: percentage
vs_last_week: percentage
vs_prediction: percentage
performance_report:
publication_id: string
project_title: string
platform: string
published_at: datetime
current_metrics:
views: number
likes: number
comments: number
shares: number
engagement_rate: percentage
historical:
views_timeline: DataPoint[]
engagement_timeline: DataPoint[]
analysis:
performance_tier: string
vs_prediction: percentage
vs_category_average: percentage
best_performing_aspect: string
improvement_areas: string[]
insights:
what_worked: string[]
what_could_improve: string[]
recommendations: string[]
feedback_loop:
data_collected:
- content_features (topic, style, duration)
- timing_features (day, hour, competition)
- predicted_metrics
- actual_metrics
learning_updates:
frequency: weekly
process:
1. Calculate prediction accuracy
2. Identify systematic biases
3. Update prediction weights
4. Refine optimal timing data
5. Update content recommendations
model_improvements:
- Adjust trend weights
- Refine audience patterns
- Update platform algorithms assumptions
- Incorporate new success patterns
notification_triggers:
milestones:
views:
- 100: "First 100 views!"
- 1000: "1K views milestone!"
- 10000: "10K views - going viral!"
engagement:
- first_comment: "First comment received"
- engagement_10: "10% engagement rate"
alerts:
viral_potential:
condition: view_velocity > 500/hour
message: "Content showing viral potential!"
priority: high
underperforming:
condition: views < predicted * 0.5 after 24h
message: "Content below expectations"
suggestions: [boost, reshare, analyze]
issue_detected:
condition: sudden_drop or negative_comments_spike
message: "Potential issue detected"
priority: high
tracking_result:
publication_id: string
platform: string
fetched_at: datetime
metrics:
views: number
likes: number
comments: number
shares: number
engagement_rate: percentage
deltas:
since_last_fetch:
views: +number
engagement: +percentage
since_publish:
hours: number
total_views: number
analysis:
performance_tier: string
viral_signals: bool
prediction_accuracy: percentage
actions:
notifications_sent: Event[]
model_updates: Update[]
inputs_from:
- content_scheduler (publication events)
- publications_table (metadata)
- market_researcher (predictions)
outputs_to:
- analytics_table (database)
- market_researcher (learning feedback)
- notification_system
- reporting_dashboard
apis:
- YouTube Analytics API
- TikTok Analytics API
- Supabase (storage)
Agent Version: 1.0.0 Last Updated: 2025-12-23