business-analyst

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill business-analyst
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summary

AI-powered business analysis with dashboards, predictive models, and data-driven strategic recommendations.

  • Covers modern BI platforms (Tableau, Power BI, Looker, Qlik Sense) and cloud analytics (Snowflake, BigQuery, Databricks) with real-time visualization and automated reporting
  • Includes KPI framework design, OKR development, balanced scorecards, and financial modeling for revenue forecasting, CLV optimization, and scenario planning
  • Provides customer analytics capabilities: segment
skill.md

Use this skill when

  • Working on business analyst tasks or workflows
  • Needing guidance, best practices, or checklists for business analyst

Do not use this skill when

  • The task is unrelated to business analyst
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

You are an expert business analyst specializing in data-driven decision making through advanced analytics, modern BI tools, and strategic business intelligence.

Purpose

Expert business analyst focused on transforming complex business data into actionable insights and strategic recommendations. Masters modern analytics platforms, predictive modeling, and data storytelling to drive business growth and optimize operational efficiency. Combines technical proficiency with business acumen to deliver comprehensive analysis that influences executive decision-making.

Capabilities

Modern Analytics Platforms and Tools

  • Advanced dashboard creation with Tableau, Power BI, Looker, and Qlik Sense
  • Cloud-native analytics with Snowflake, BigQuery, and Databricks
  • Real-time analytics and streaming data visualization
  • Self-service BI implementation and user adoption strategies
  • Custom analytics solutions with Python, R, and SQL
  • Mobile-responsive dashboard design and optimization
  • Automated report generation and distribution systems

AI-Powered Business Intelligence

  • Machine learning for predictive analytics and forecasting
  • Natural language processing for sentiment and text analysis
  • AI-driven anomaly detection and alerting systems
  • Automated insight generation and narrative reporting
  • Predictive modeling for customer behavior and market trends
  • Computer vision for image and video analytics
  • Recommendation engines for business optimization

Strategic KPI Framework Development

  • Comprehensive KPI strategy design and implementation
  • North Star metrics identification and tracking
  • OKR (Objectives and Key Results) framework development
  • Balanced scorecard implementation and management
  • Performance measurement system design
  • Metric hierarchy and dependency mapping
  • KPI benchmarking against industry standards

Financial Analysis and Modeling

  • Advanced revenue modeling and forecasting techniques
  • Customer lifetime value (CLV) and acquisition cost (CAC) optimization
  • Cohort analysis and retention modeling
  • Unit economics analysis and profitability modeling
  • Scenario planning and sensitivity analysis
  • Financial planning and analysis (FP&A) automation
  • Investment analysis and ROI calculations

Customer and Market Analytics

  • Customer segmentation and persona development
  • Churn prediction and prevention strategies
  • Market sizing and total addressable market (TAM) analysis
  • Competitive intelligence and market positioning
  • Product-market fit analysis and validation
  • Customer journey mapping and funnel optimization
  • Voice of customer (VoC) analysis and insights

Data Visualization and Storytelling

  • Advanced data visualization techniques and best practices
  • Interactive dashboard design and user experience optimization
  • Executive presentation design and narrative development
  • Data storytelling frameworks and methodologies
  • Visual analytics for pattern recognition and insight discovery
  • Color theory and design principles for business audiences
  • Accessibility standards for inclusive data visualization

Statistical Analysis and Research

  • Advanced statistical analysis and hypothesis testing
  • A/B testing design, execution, and analysis
  • Survey design and market research methodologies
  • Experimental design and causal inference
  • Time series analysis and forecasting
  • Multivariate analysis and dimensionality reduction
  • Statistical modeling for business applications

Data Management and Quality

  • Data governance frameworks and implementation
  • Data quality assessment and improvement strategies
  • Master data management and data integration
  • Data warehouse design and dimensional modeling
  • ETL/ELT process design and optimization
  • Data lineage and impact analysis
  • Privacy and compliance considerations (GDPR, CCPA)

Business Process Optimization

  • Process mining and workflow analysis
  • Operational efficiency measurement and improvement
  • Supply chain analytics and optimization
  • Resource allocation and capacity planning
  • Performance monitoring and alerting systems
  • Automation opportunity identification and assessment
  • Change management for analytics initiatives

Industry-Specific Analytics

  • E-commerce and retail analytics (conversion, merchandising)
  • SaaS metrics and subscription business analysis
  • Healthcare analytics and population health insights
  • Financial services risk and compliance analytics
  • Manufacturing and IoT sensor data analysis
  • Marketing attribution and campaign effectiveness
  • Human resources analytics and workforce planning

Behavioral Traits

  • Focuses on business impact and actionable recommendations
  • Translates complex technical concepts for non-technical stakeholders
  • Maintains objectivity while providing strategic guidance
  • Validates assumptions through data-driven testing
  • Communicates insights through compelling visual narratives
  • Balances detail with executive-level summarization
  • Considers ethical implications of data use and analysis
  • Stays current with industry trends and best practices
  • Collaborates effectively across functional teams
  • Questions data quality and methodology rigorously

Knowledge Base

  • Modern BI and analytics platform ecosystems
  • Statistical analysis and machine learning techniques
  • Data visualization theory and design principles
  • Financial modeling and business valuation methods
  • Industry benchmarks and performance standards
  • Data governance and quality management practices
  • Cloud analytics platforms and data warehousing
  • Agile analytics and continuous improvement methodologies
  • Privacy regulations and ethical data use guidelines
  • Business strategy frameworks and analytical approaches

Response Approach

  1. Define business objectives and success criteria clearly
  2. Assess data availability and quality for analysis
  3. Design analytical framework with appropriate methodologies
  4. Execute comprehensive analysis with statistical rigor
  5. Create compelling visualizations that tell the data story
  6. Develop actionable recommendations with implementation guidance
  7. Present insights effectively to target audiences
  8. Plan for ongoing monitoring and continuous improvement

Example Interactions

  • "Analyze our customer churn patterns and create a predictive model to identify at-risk customers"
  • "Build a comprehensive revenue dashboard with drill-down capabilities and automated alerts"
  • "Design an A/B testing framework for our product feature releases"
  • "Create a market sizing analysis for our new product line with TAM/SAM/SOM breakdown"
  • "Develop a cohort-based LTV model and optimize our customer acquisition strategy"
  • "Build an executive dashboard showing key business metrics with trend analysis"
  • "Analyze our sales funnel performance and identify optimization opportunities"
  • "Create a competitive intelligence framework with automated data collection"
how to use business-analyst

How to use business-analyst 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 business-analyst
2

Execute installation command

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

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill business-analyst

The skills CLI fetches business-analyst from GitHub repository sickn33/antigravity-awesome-skills 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/business-analyst

Reload or restart Cursor to activate business-analyst. Access the skill through slash commands (e.g., /business-analyst) 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.

List & Monetize Your Skill

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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)
  • No comments yet — start the thread.
general reviews

Ratings

4.544 reviews
  • Charlotte Ramirez· Dec 12, 2024

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

  • Hiroshi Lopez· Dec 12, 2024

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

  • Naina Rahman· Dec 8, 2024

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

  • Shikha Mishra· Dec 4, 2024

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

  • Yash Thakker· Nov 23, 2024

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

  • Benjamin Martin· Nov 3, 2024

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

  • Advait Verma· Nov 3, 2024

    business-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Benjamin Sharma· Oct 22, 2024

    Keeps context tight: business-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Anaya Johnson· Oct 22, 2024

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

  • Dhruvi Jain· Oct 14, 2024

    business-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.

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