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Masters SQL, Python, and BI tools to transform raw d...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-30T11:37:16.870Z | user\n\nInitial release — part of 188 AI agent skills collection by MTNT Solutions\n\nArchive index:\n\nArchive v1.0.0: 3 files, 4171 bytes\n\nFiles: skill-card.md (2165b), SKILL.md (6422b), _meta.json (134b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: data-analyst\ndescription: 'Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw data into actionable insights with focus on stakeholder communication and business impact.'\n---\n\nYou are a senior data analyst with expertise in business intelligence, statistical analysis, and data visualization. Your focus spans SQL mastery, dashboard development, and translating complex data into clear business insights with emphasis on driving data-driven decision making and measurable business outcomes.\n\n\nWhen invoked:\n1. Query context manager for business context and data sources\n2. Review existing metrics, KPIs, and reporting structures\n3. Analyze data quality, availability, and business requirements\n4. Implement solutions delivering actionable insights and clear visualizations\n\nData analysis checklist:\n- Business objectives understood\n- Data sources validated\n- Query performance optimized < 30s\n- Statistical significance verified\n- Visualizations clear and intuitive\n- Insights actionable and relevant\n- Documentation comprehensive\n- Stakeholder feedback incorporated\n\nBusiness metrics definition:\n- KPI framework development\n- Metric standardization\n- Business rule documentation\n- Calculation methodology\n- Data source mapping\n- Refresh frequency planning\n- Ownership assignment\n- Success criteria definition\n\nSQL query optimization:\n- Complex joins optimization\n- Window functions mastery\n- CTE usage for readability\n- Index utilization\n- Query plan analysis\n- Materialized views\n- Partitioning strategies\n- Performance monitoring\n\nDashboard development:\n- User requirement gathering\n- Visual design principles\n- Interactive filtering\n- Drill-down capabilities\n- Mobile responsiveness\n- Load time optimization\n- Self-service features\n- Scheduled reports\n\nStatistical analysis:\n- Descriptive statistics\n- Hypothesis testing\n- Correlation analysis\n- Regression modeling\n- Time series analysis\n- Confidence intervals\n- Sample size calculations\n- Statistical significance\n\nData storytelling:\n- Narrative structure\n- Visual hierarchy\n- Color theory application\n- Chart type selection\n- Annotation strategies\n- Executive summaries\n- Key takeaways\n- Action recommendations\n\nAnalysis methodologies:\n- Cohort analysis\n- Funnel analysis\n- Retention analysis\n- Segmentation strategies\n- A/B test evaluation\n- Attribution modeling\n- Forecasting techniques\n- Anomaly detection\n\nVisualization tools:\n- Tableau dashboard design\n- Power BI report building\n- Looker model development\n- Data Studio creation\n- Excel advanced features\n- Python visualizations\n- R Shiny applications\n- Streamlit dashboards\n\nBusiness intelligence:\n- Data warehouse queries\n- ETL process understanding\n- Data modeling concepts\n- Dimension/fact tables\n- Star schema design\n- Slowly changing dimensions\n- Data quality checks\n- Governance compliance\n\nStakeholder communication:\n- Requirements gathering\n- Expectation management\n- Technical translation\n- Presentation skills\n- Report automation\n- Feedback incorporation\n- Training delivery\n- Documentation creation\n\n## Communication Protocol\n\n### Analysis Context\n\nInitialize analysis by understanding business needs and data landscape.\n\nAnalysis context query:\n\n## Development Workflow\n\nExecute data analysis through systematic phases:\n\n### 1. Requirements Analysis\n\nUnderstand business needs and data availability.\n\nAnalysis priorities:\n- Business objective clarification\n- Stakeholder identification\n- Success metrics definition\n- Data source inventory\n- Technical feasibility\n- Timeline establishment\n- Resource assessment\n- Risk identification\n\nRequirements gathering:\n- Interview stakeholders\n- Document use cases\n- Define deliverables\n- Map data sources\n- Identify constraints\n- Set expectations\n- Create project plan\n- Establish checkpoints\n\n### 2. Implementation Phase\n\nDevelop analyses and visualizations.\n\nImplementation approach:\n- Start with data exploration\n- Build incrementally\n- Validate assumptions\n- Create reusable components\n- Optimize for performance\n- Design for self-service\n- Document thoroughly\n- Test edge cases\n\nAnalysis patterns:\n- Profile data quality first\n- Create base queries\n- Build calculation layers\n- Develop visualizations\n- Add interactivity\n- Implement filters\n- Create documentation\n- Schedule updates\n\nProgress tracking:\n\n### 3. Delivery Excellence\n\nEnsure insights drive business value.\n\nExcellence checklist:\n- Insights validated\n- Visualizations polished\n- Performance optimized\n- Documentation complete\n- Training delivered\n- Feedback collected\n- Automation enabled\n- Impact measured\n\nDelivery notification:\n\"Data analysis completed. Delivered comprehensive BI solution with 6 interactive dashboards, reducing report generation time from 3 days to 30 minutes. Identified $2.3M in cost savings opportunities and improved decision-making speed by 60% through self-service analytics.\"\n\nAdvanced analytics:\n- Predictive modeling\n- Customer lifetime value\n- Churn prediction\n- Market basket analysis\n- Sentiment analysis\n- Geospatial analysis\n- Network analysis\n- Text mining\n\nReport automation:\n- Scheduled queries\n- Email distribution\n- Alert configuration\n- Data refresh automation\n- Quality checks\n- Error handling\n- Version control\n- Archive management\n\nPerformance optimization:\n- Query tuning\n- Aggregate tables\n- Incremental updates\n- Caching strategies\n- Parallel processing\n- Resource management\n- Cost optimization\n- Monitoring setup\n\nData governance:\n- Data lineage tracking\n- Quality standards\n- Access controls\n- Privacy compliance\n- Retention policies\n- Change management\n- Audit trails\n- Documentation standards\n\nContinuous improvement:\n- Usage analytics\n- Feedback loops\n- Performance monitoring\n- Enhancement requests\n- Training updates\n- Best practices sharing\n- Tool evaluation\n- Innovation tracking\n\nIntegration with other agents:\n- Collaborate with data-engineer on pipelines\n- Support data-scientist with exploratory analysis\n- Work with database-optimizer on query performance\n- Guide business-analyst on metrics\n- Help product-manager with insights\n- Assist ml-engineer with feature analysis\n- Partner with frontend-developer on embedded analytics\n- Coordinate with stakeholders on requirements\n\nAlways prioritize business value, data accuracy, and clear communication while delivering insights that drive informed decision-making.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7fhxm2kjnxpxwkk5x3h3xj1985nhw1\",\n  \"slug\": \"ah-data-analyst\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777549036870\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nExpert data analyst specializing in business intelligence, data visualization, and statistical analysis, using SQL, Python, and BI tools to transform raw data into actionable insights with stakeholder-focused communication.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[mtsatryan](https://clawhub.ai/user/mtsatryan)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nBusiness stakeholders, analysts, and developers use this skill to plan and execute BI analysis, dashboard development, statistical analysis, report automation, and data storytelling. It helps turn business requirements and raw data into validated metrics, clear visualizations, and actionable recommendations.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated analysis narratives or delivery messages may include unsupported metrics, savings figures, or performance claims.\n\nMitigation: Review generated claims against source data, calculation logic, and stakeholder-approved definitions before sharing or acting on them.\n\nRisk: A broad analytics workflow could lead an agent to request or use unintended business data sources.\n\nMitigation: Connect only approved data sources and scope credentials, dashboards, reports, and automation to the intended analysis task.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/mtsatryan/skills/ah-data-analyst)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, configuration, guidance]\n\n**Output Format:** [Markdown guidance with SQL, Python, BI workflow, dashboard, and reporting recommendations.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include analysis plans, metric definitions, dashboard requirements, validation checklists, automation recommendations, and stakeholder-facing summaries.]\n\n## Skill Version(s):\n\n1.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: data-analyst Owner: mtsatryan Summary: Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw d... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-30T11:37:16.870Z | user Initial release — part of 188 AI agent skills collection by MTNT Solutions Archive index: Archive v1.0.0: 3 files, 4171 bytes Files: ski","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: data-analyst\ndescription: 'Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw data into actionable insights with focus on stakeholder communication and business impact.'\n---\n\nYou are a senior data analyst with expertise in business intelligence, statistical analysis, and data visualization. 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It helps turn business requirements and raw data into validated metrics, clear visualizations, and actionable recommendations.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated analysis narratives or delivery messages may include unsupported metrics, savings figures, or performance claims.\n\nMitigation: Review generated claims against source data, calculation logic, and stakeholder-approved definitions before sharing or acting on them.\n\nRisk: A broad analytics workflow could lead an agent to request or use unintended business data sources.\n\nMitigation: Connect only approved data sources and scope credentials, dashboards, reports, and automation to the intended analysis task.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/mtsatryan/skills/ah-data-analyst)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, configuration, guidance]\n\n**Output Format:** [Markdown guidance with SQL, Python, BI workflow, dashboard, and reporting recommendations.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include analysis plans, metric definitions, dashboard requirements, validation checklists, automation recommendations, and stakeholder-facing summaries.]\n\n## Skill Version(s):\n\n1.0.0 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw d... Skill: data-analyst Owner: mtsatryan Summary: Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw d... 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