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analytics-expert▌

shipshitdev/library · updated Jun 2, 2026

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$npx skills add https://github.com/shipshitdev/library --skill analytics-expert
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summary

This skill enables Claude to analyze content analytics data, generate comprehensive reports, identify performance trends, calculate ROI and revenue attribution, and provide actionable insights for content optimization.

skill.md

Content Analytics Expert

Overview

This skill enables Claude to analyze content analytics data, generate comprehensive reports, identify performance trends, calculate ROI and revenue attribution, and provide actionable insights for content optimization.

When to Use This Skill

This skill activates automatically when users:

  • Ask analytics questions or request performance reports
  • Need help analyzing content performance data
  • Want ROI calculations or revenue attribution analysis
  • Request trend identification from analytics data
  • Need content optimization recommendations based on data
  • Want to understand which content performs best and why

Core Capabilities

1. Generate Analytics Reports

To generate comprehensive analytics reports:

  1. Collect Analytics Data

    • Access analytics platform data (discover from project)
    • Aggregate performance metrics across platforms
    • Gather engagement data (views, likes, comments, shares)
    • Collect conversion and revenue data (if available)

  2. Create Report Structure

    • Weekly/Monthly performance reports
    • Platform-specific performance analysis
    • Content type performance comparison
    • Audience engagement reports
    • ROI and revenue attribution reports

  3. Generate Report Content

    • Summarize key metrics and insights
    • Create data visualizations (charts, graphs)
    • Identify top-performing content
    • Highlight trends and patterns
    • Provide actionable recommendations

Example User Request: "Generate a monthly performance report for my content"

Integration (discover from project):

  • Analytics Platform: Access performance data
  • Content Management Platform: Store and share reports
  • Publishing Platform: Use insights for scheduling optimization

2. Identify Top-Performing Content Patterns

To identify patterns in top-performing content:

  1. Analyze Performance Data

    • Review content performance metrics
    • Identify top-performing content pieces
    • Analyze common characteristics of successful content

  2. Extract Patterns

    • Content topics and themes
    • Content formats and types
    • Posting times and frequencies
    • Platform-specific patterns
    • Engagement drivers (hooks, CTAs, visuals)

  3. Generate Insights

    • Document successful content patterns
    • Recommend content strategies based on patterns
    • Suggest content replication opportunities

Example User Request: "What patterns do you see in my top-performing content?"

Integration (discover from project):

  • Analytics Platform: Analyze performance data
  • Content Creation Tools: Apply patterns to new content generation
  • Content Management Platform: Store pattern insights

3. Predict Content Performance

To predict content performance before publishing:

  1. Analyze Historical Data

    • Review similar content performance
    • Identify factors that correlate with success
    • Build performance prediction models

  2. Evaluate New Content

    • Compare new content to historical patterns
    • Assess content against success factors
    • Calculate predicted performance scores

  3. Provide Recommendations

    • Suggest content improvements
    • Recommend optimal posting times
    • Identify best platforms for content
    • Predict viral potential

Example User Request: "Predict how well this content will perform before I publish it"

Integration (discover from project):

  • Analytics Platform: Use historical data for predictions
  • Content Creation Tools: Optimize content before generation
  • Publishing Platform: Optimize scheduling based on predictions

4. ROI Analysis and Attribution

To calculate ROI and revenue attribution:

  1. Track Revenue Metrics

    • Link content to conversions and revenue
    • Track attribution through project's tracking links (discover format from project docs)
    • Calculate cost per content piece (API costs, time)

  2. Calculate ROI

    • Revenue per content piece
    • Cost to create content
    • ROI percentage calculation
    • Revenue per platform/channel

  3. Generate ROI Reports

    • Content-level ROI analysis
    • Platform ROI comparison
    • Campaign ROI tracking
    • Revenue optimization recommendations

Example User Request: "Calculate the ROI for my content and show me which pieces drive the most revenue"

Integration (discover from project):

  • Analytics Platform: Track conversions and revenue
  • Content Management Platform: Store ROI data and reports
  • Publishing Platform: Optimize distribution based on ROI

5. Trend Identification

To identify trends from analytics data:

  1. Analyze Time-Series Data

    • Review performance trends over time
    • Identify growth or decline patterns
    • Detect seasonal trends

  2. Identify Emerging Trends

    • Content topics gaining traction
    • Platform trends and shifts
    • Audience behavior changes
    • Engagement pattern shifts

  3. Provide Trend Insights

    • Document identified trends
    • Recommend actions based on trends
    • Predict future trend directions

Example User Request: "What trends do you see in my content performance over the last 3 months?"

Integration (discover from project):

  • Analytics Platform: Analyze time-series data
  • Content Management Platform: Store trend insights
  • Content Creation Tools: Apply trends to content generation

Project Context Discovery

Before analyzing analytics, discover the project's context:

  1. Scan Project Documentation:

    • Check .agents/SYSTEM/ARCHITECTURE.md for analytics platform details
    • Review .agents/SYSTEM/SUMMARY.md for analytics capabilities
    • Look for analytics-related documentation

  2. Identify Analytics Platform:

    • Check for analytics service integrations in codebase
    • Look for analytics API endpoints or SDKs
    • Review environment variables for analytics services

  3. Discover Available Metrics:

    • Review analytics API documentation if available
    • Check for analytics data models or schemas
    • Identify what metrics the project tracks

Common Analytics Data Types (adapt based on discovery):

  • Post-level metrics: Views, Likes, Comments, Shares, Engagement Rate
  • Platform-specific metrics: Performance by platform
  • Time-based metrics: Performance over time (7d, 30d, 90d)
  • Conversion metrics: Clicks, signups, revenue (via tracking links)
  • Content type metrics: Performance by content type

Key Metrics:

  • Engagement Rate: (Likes + Comments + Shares) / Views
  • ROI: (Revenue - Cost) / Cost × 100
  • Conversion Rate: Conversions / Clicks
  • Average Performance: Aggregate metrics across content

Best Practices

  1. Data-Driven Insights: Base all recommendations on actual analytics data
  2. Context Matters: Consider platform, timing, and audience when analyzing data
  3. Actionable Recommendations: Provide specific, actionable insights, not just data
  4. Comparative Analysis: Compare performance against benchmarks and historical data
  5. Continuous Monitoring: Recommend regular analytics review and optimization

Resources

references/

  • analytics-api-reference.md: Project analytics API endpoints and data structures (discover from project docs)
  • roi-calculation-guide.md: ROI calculation methods and formulas
  • performance-benchmarks.md: Industry benchmarks for content performance

assets/

  • analytics-report-template.md: Template for analytics reports
  • roi-report-template.md: Template for ROI analysis reports
  • trend-analysis-template.md: Template for trend identification reports

Complementary Skills (External)

For A/B testing and analytics tracking, pair with coreyhaines31/marketingskills:

/plugin marketplace add coreyhaines31/marketingskills
Skill Why
analytics-tracking Tracking setup and event configuration
ab-test-setup A/B test design and implementation
how to use analytics-expert

How to use analytics-expert on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add analytics-expert
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/shipshitdev/library --skill analytics-expert

The skills CLI fetches analytics-expert from GitHub repository shipshitdev/library and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analytics-expert

Reload or restart Cursor to activate analytics-expert. Access the skill through slash commands (e.g., /analytics-expert) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.6★★★★★54 reviews
  • ★★★★★Diego Haddad· Dec 24, 2024

    analytics-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Evelyn Jain· Dec 24, 2024

    Solid pick for teams standardizing on skills: analytics-expert is focused, and the summary matches what you get after install.

  • ★★★★★Hana Menon· Dec 20, 2024

    Registry listing for analytics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Diego Khan· Dec 16, 2024

    Useful defaults in analytics-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Sakshi Patil· Nov 15, 2024

    analytics-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Michael Wang· Nov 15, 2024

    We added analytics-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Valentina Ghosh· Nov 15, 2024

    Registry listing for analytics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Olivia Garcia· Nov 11, 2024

    Solid pick for teams standardizing on skills: analytics-expert is focused, and the summary matches what you get after install.

  • ★★★★★Diego Reddy· Nov 7, 2024

    I recommend analytics-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Diego Anderson· Oct 26, 2024

    Solid pick for teams standardizing on skills: analytics-expert is focused, and the summary matches what you get after install.

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