Vibeship-spawner-skills analytics

Analytics AI Tools

install

source · Clone the upstream repo

git clone https://github.com/vibeforge1111/vibeship-spawner-skills

manifest: ai-tools/analytics/skill.yaml

Analytics AI Tools

Product analytics, data analysis, and business intelligence

id: analytics-ai-tools name: Analytics AI Tools version: "1.0.0" category: ai-tools

description: | Master the AI-powered analytics tools that help you understand users, make data-driven decisions, and grow your business intelligently. From product analytics to natural language data analysis.

business_value: time_saved: "10-20 hours/week on analytics and reporting" cost_saved: "$5,000-15,000/month vs data analyst hires" revenue_impact: "Data-driven decisions = 20-40% better outcomes" competitive_edge: "Understand users deeper than competitors"

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TOOL COMPARISON

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tools: product_analytics: - id: amplitude name: Amplitude website: https://amplitude.com best_for: "Product analytics with AI insights" pricing: free_tier: true free_limits: "10M events/month" paid_starts: "$49/month" strengths: - "Best-in-class behavioral analytics" - "AI-powered insights and anomaly detection" - "Excellent cohort and retention analysis" - "Strong experimentation integration" weaknesses: - "Learning curve for advanced features" - "Can get expensive at scale" - "Event volume can explode unexpectedly" best_for_stage: "Series A+ with product-market fit focus"

- id: mixpanel
  name: Mixpanel
  website: https://mixpanel.com
  best_for: "Event tracking with marketing focus"
  pricing:
    free_tier: true
    free_limits: "20M events/month"
    paid_starts: "$28/month"
  strengths:
    - "Generous free tier"
    - "Great for marketing analytics"
    - "Easy to set up"
    - "Good mobile analytics"
  weaknesses:
    - "Less powerful than Amplitude for product"
    - "Limited AI features compared to competitors"
  best_for_stage: "Early stage, mobile-first apps"

- id: posthog
  name: PostHog
  website: https://posthog.com
  best_for: "Open source, all-in-one analytics"
  pricing:
    free_tier: true
    free_limits: "1M events/month + session recordings"
    paid_starts: "$0 (usage-based)"
    self_hosted: true
  strengths:
    - "Open source, can self-host"
    - "Analytics + session recordings + feature flags"
    - "Privacy-friendly (EU hosting, self-host)"
    - "Generous free tier"
  weaknesses:
    - "Fewer integrations than established players"
    - "Newer, less mature"
  best_for_stage: "Privacy-conscious, technical teams"

- id: heap
  name: Heap
  website: https://heap.io
  best_for: "Auto-capture everything"
  pricing:
    free_tier: true
    paid_starts: "Custom"
  strengths:
    - "Retroactive analytics (captures everything)"
    - "No manual event tracking needed"
    - "Great for discovering unknown patterns"
  weaknesses:
    - "Data can be noisy"
    - "Expensive at scale"
    - "Less control over what's tracked"
  best_for_stage: "Teams who forgot to track events"

- id: june
  name: June
  website: https://june.so
  best_for: "B2B SaaS analytics"
  pricing:
    free_tier: true
    free_limits: "1,000 users"
    paid_starts: "$149/month"
  strengths:
    - "Built specifically for B2B SaaS"
    - "Company-level analytics (not just users)"
    - "Beautiful, simple UI"
    - "Quick setup with Segment"
  weaknesses:
    - "Limited for non-SaaS"
    - "Fewer advanced features"
  best_for_stage: "B2B SaaS startups"

data_analysis: - id: julius name: Julius AI website: https://julius.ai best_for: "Chat with your data" pricing: free_tier: true paid_starts: "$20/month" strengths: - "Natural language queries" - "Handles CSV, Excel, databases" - "Creates visualizations automatically" - "No SQL or Python needed" weaknesses: - "Limited for complex analysis" - "Can misinterpret ambiguous questions" best_for_stage: "Non-technical teams needing data insights"

- id: hex
  name: Hex
  website: https://hex.tech
  best_for: "Collaborative data notebooks"
  pricing:
    free_tier: true
    paid_starts: "$80/month"
  strengths:
    - "SQL + Python + AI in one place"
    - "Beautiful shareable reports"
    - "Version control for analysis"
    - "Great for data teams"
  weaknesses:
    - "Requires technical skills"
    - "Expensive for small teams"
  best_for_stage: "Data teams at growth companies"

- id: obviously-ai
  name: Obviously AI
  website: https://obviously.ai
  best_for: "No-code machine learning"
  pricing:
    paid_starts: "$75/month"
  strengths:
    - "Build ML models without code"
    - "Predictions on your data"
    - "Churn prediction, lead scoring"
  weaknesses:
    - "Limited customization"
    - "Black box models"
  best_for_stage: "Business teams wanting ML predictions"

bi_platforms: - id: metabase name: Metabase website: https://metabase.com best_for: "Open source BI" pricing: free_tier: true self_hosted: true cloud_starts: "$85/month" strengths: - "Free and open source" - "Easy to self-host" - "Simple for non-technical users" - "Good for internal dashboards" weaknesses: - "Limited AI features" - "Less powerful than enterprise tools" best_for_stage: "Early stage, budget-conscious"

- id: tableau-ai
  name: Tableau AI
  website: https://tableau.com
  best_for: "Enterprise visualization"
  pricing:
    paid_starts: "$15/user/month"
  strengths:
    - "Best-in-class visualizations"
    - "Einstein AI integration"
    - "Handles massive datasets"
  weaknesses:
    - "Expensive"
    - "Complex to master"
  best_for_stage: "Enterprise with dedicated analysts"

- id: thoughtspot
  name: ThoughtSpot
  website: https://thoughtspot.com
  best_for: "Search-driven analytics"
  pricing:
    paid_starts: "Custom"
  strengths:
    - "Google-like search for data"
    - "AI-generated insights"
    - "Self-service for business users"
  weaknesses:
    - "Very expensive"
    - "Enterprise sales process"
  best_for_stage: "Large enterprises"

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DECISION GUIDE

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decision_guide: by_stage: pre_seed: recommended: posthog reason: "Free, open source, includes session recordings" alternatives: [mixpanel, june]

seed:
  recommended: amplitude
  reason: "Best product analytics, good free tier"
  alternatives: [mixpanel, posthog]

series_a:
  recommended: amplitude
  reason: "Scale with you, AI insights mature"
  alternatives: [heap]

series_b_plus:
  recommended: "amplitude + hex"
  reason: "Product analytics + data team capabilities"

by_use_case: product_analytics: primary: amplitude alternative: mixpanel budget: posthog

marketing_analytics:
  primary: mixpanel
  alternative: amplitude

b2b_saas:
  primary: june
  alternative: amplitude

data_exploration:
  primary: julius
  alternative: hex

dashboards:
  primary: metabase
  alternative: tableau-ai

ml_predictions:
  primary: obviously-ai

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MASTERY PATH

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mastery: learning_curve: medium time_to_value: "2-4 hours" time_to_proficiency: "2 weeks" time_to_mastery: "2-3 months"

onboarding_steps: - step: 1 action: "Choose your tool" details: "Use decision guide above based on stage and use case" time: "30 min"

- step: 2
  action: "Install tracking"
  details: "Add SDK or connect via Segment"
  time: "1-2 hours"

- step: 3
  action: "Define key events"
  details: "Track 10-15 critical user actions"
  time: "2 hours"

- step: 4
  action: "Build first dashboard"
  details: "Create product health metrics view"
  time: "1 hour"

- step: 5
  action: "Set up alerts"
  details: "Get notified on anomalies"
  time: "30 min"

- step: 6
  action: "Create first cohort analysis"
  details: "Compare user segments"
  time: "1 hour"

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PATTERNS (Best Practices)

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patterns:

name: "Event naming taxonomy" description: "Use Object_Action pattern consistently" example: "Button_Clicked, Form_Submitted, Item_Purchased" impact: "Makes events findable and consistent"
name: "Track properties, not events" description: "Use event properties for variants" example: "Button_Clicked with button_name property, not Button_A_Clicked" impact: "Cleaner event stream, easier analysis"
name: "Define metrics before building" description: "Know your north star and supporting metrics first" example: "North star: Weekly Active Users. Supporting: Activation, Retention, Revenue" impact: "Focus on what matters"
name: "Use cohorts for comparison" description: "Always compare segments, not just totals" example: "Compare power users vs casual users behavior" impact: "Find actionable insights"
name: "Set up anomaly alerts" description: "Get notified when metrics deviate" example: "Alert if DAU drops >10% day-over-day" impact: "Catch problems early"

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ANTI-PATTERNS (Mistakes to Avoid)

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anti_patterns:

name: "Tracking everything" description: "Adding every possible event" impact: "Noise drowns signal, quota burns fast" fix: "Track 10-20 key events deeply"
name: "No naming convention" description: "Random event names across team" impact: "Can't find events, duplicates everywhere" fix: "Document and enforce Object_Action pattern"
name: "Vanity metrics focus" description: "Celebrating page views and signups" impact: "Misleading success signals" fix: "Focus on activation, retention, revenue"
name: "Ignoring funnels" description: "Only looking at end conversion" impact: "Can't identify where users drop off" fix: "Build funnels for every key flow"
name: "No user identity" description: "Not connecting anonymous to logged-in users" impact: "Fragmented user journey, inflated counts" fix: "Implement proper identify() on login"

triggers:

"need product analytics"
"understand user behavior"
"track events"
"funnel analysis"
"retention metrics"
"cohort analysis"
"data dashboard"
"business intelligence"

tags:

analytics
product
data
metrics
dashboards
business-intelligence