Data analysis skills for AI agents.

The library's data analysis skills include A/B Testing Specialist, Causal Inference Practitioner and Cohort Analyst. They cover statistics and modeling, spreadsheets, exploratory data analysis, and data engineering. 65 passed Skill Guard clean. The other 1 switch on only after you have read them and said yes. 19 ready-made teams use skills from this list, such as Customer Success Org and Marketing Agency. Skills are written instructions, not app connections: a bot reads the one a job calls for and works the way it lays out. Add any of them to a bot in Settings → Skills, or tell your Chief of Staff the job and let it choose.

• A/B Testing SpecialistEnd-to-end guide for designing, running, and analyzing A/B tests including experiment design, statistical significance and sample size calculation.
• Causal Inference PractitionerCausal inference expertise covering A/B testing limitations and extensions, instrumental variables and difference-in-differences estimation.
• Cohort AnalystGuide for cohort-based analytics including retention analysis, customer lifetime value calculation, behavioral segmentation and funnel analysis.
• CSV Data CleanerQuick techniques for cleaning CSV files - deduplication, normalization, encoding fixes and merging.
• Dashboard DesignerGuide for designing effective analytical dashboards using Metabase, Grafana, and Looker including design principles, KPI selection and layout patterns.
• Data StorytellerGuide for translating data analysis into compelling narratives including insight framing, presentation design and audience adaptation.
• Data Visualization ArtistComprehensive guide to creating effective data visualizations with matplotlib, plotly, seaborn, and D3.js including chart selection frameworks.
• Geospatial AnalystGeospatial analysis expertise covering GIS fundamentals, spatial data types and coordinate systems, spatial queries and joins.
• Jupyter Workflow MasterGuide for productive Jupyter notebook workflows including kernel management, reproducibility patterns, nbconvert pipelines and parameterized execution.
• Pandas Power UserAdvanced pandas techniques for data wrangling including performance optimization, method chaining patterns, MultiIndex operations and memory management.
• Python Data ScientistGuide for applied machine learning with scikit-learn covering feature engineering, model selection, pipeline construction and evaluation.
• SaaS Metrics AnalystSaaS business metrics analysis covering MRR, ARR, churn rates, customer lifetime value, cohort analysis, unit economics, and dashboard design.
• SQL Analytics ExpertAdvanced SQL for analytics covering window functions, CTEs, recursive queries, query optimization and pivoting.
• Statistical AnalystApplied statistics guide covering hypothesis testing, regression analysis, ANOVA, confidence intervals and p-value interpretation.
• Time Series AnalystTime series analysis expertise covering decomposition methods, ARIMA and SARIMA modeling, Prophet for business forecasting and seasonality detection.
• A/B Test Design (Data analysis)Designs an A/B test from scratch.
• API Data ExtractionProduces an API data extraction design covering authentication approach, pagination strategy, rate limit handling and response parsing specification.
• BI Dashboard SpecificationProduces a BI dashboard specification for a named business function including dashboard purpose, audience, KPIs displayed, panel layout and refresh cadence.
• Business Reporting CadenceDesigns a business reporting cadence with weekly, monthly, and quarterly report structures including audience, content and format.
• Chart FormattingProduces a complete chart formatting specification including axis labels, tick mark spacing, gridline density, legend placement and annotation placement.
• Chart Type SelectorDetermines the correct chart type for a specific data comparison by applying chart selection logic based on analytical intent (ranking, trend, distribution, composition, correlation, geographic).
• Cohort AnalysisApplies cohort analysis to a described dataset by defining the cohort grouping variable, time horizon and measured behavior.
• Color In DataApplies color to data visualizations by selecting sequential, diverging, or categorical palettes based on data type.
• Competitive IntelligenceProduces a competitive intelligence framework: defines the competitors to track, the intelligence dimensions (product, pricing, marketing and hiring).
• Conditional Formatting RulesProduces a conditional formatting rule set for a specific use case.
• Correlation AnalysisProduces a correlation analysis between specified variables.
• Dashboard DesignProduces a complete dashboard layout specification.
• Data Catalog SetupProduces a data catalog structure with table documentation templates, column-level lineage format, and governance tagging taxonomy.
• Data Cleaning ProtocolGenerates a priority-ordered data cleaning protocol for a specific dataset.
• Data Pipeline MonitoringDesigns a data pipeline monitoring plan with health metrics (latency, error rate, row count variance), alert thresholds and on-call escalation path.
• Data ProfilingProduces a comprehensive data profile for a table or dataset including schema documentation, data type validation and uniqueness analysis.
• Data Quality RulesProduces a data quality rule set defining completeness, validity, consistency, accuracy, and uniqueness rules for each field in a dataset.
• Data Schema DesignProduces a relational or dimensional data schema with entities, attributes, data types, keys, relationships and normalization decisions with rationale.
• Data StorytellingStructures a data narrative that turns analytical findings into a compelling story.
• Data Validation SetupProduces a data validation rule set for a spreadsheet.
• Data Warehouse DesignDesigns a data warehouse structure by selecting star or snowflake schema, defining fact tables and dimension tables and specifying grain for each fact table.
• EDA FrameworkProduces a structured exploratory data analysis report covering row counts, column inventory, missing values, distributions, and frequency counts.
• ETL Pipeline DesignDesigns an ETL pipeline specification covering extract (source, frequency, authentication), transform (cleaning, joins and aggregations).
• Excel Lookup FormulasProduces working Excel/Sheets lookup formulas for specific use cases.
• Financial Model TemplateBuilds a spreadsheet financial model structure with input, calculation, and output sections.
• Forecast ModelBuilds a forecasting model structure by selecting the method (moving average, linear trend and seasonal decomposition) based on data characteristics.
• Funnel AnalysisProduces a funnel analysis with defined conversion stages, step-by-step conversion rates and biggest drop-off identification.
• Google Sheets AdvancedProduces Google Sheets-specific advanced formula patterns including ARRAYFORMULA, IMPORTRANGE, QUERY function syntax and Apps Script trigger patterns.
• Hypothesis TestingWalks through hypothesis testing for a specific business question.
• Infographic PlanningPlans an infographic by defining the key message and selecting the information hierarchy.
• KPI DefinitionProduces a complete KPI definition document with metric name, business question, formula, data source, owner, update frequency and baseline.
• KPI VisualizationDesigns the visualization layer for KPIs by selecting the display format (scorecard, gauge, sparkline, trend line) and specifying target lines.
• Cohort and Retention
• Funnel Diagnosis
• Marketing Attribution
• North Star and Experiments
• Metric FrameworkDesigns a metric framework with a goal hierarchy mapping north star metric to primary metrics to diagnostic metrics.
• Outlier DetectionIdentifies outliers in a dataset using IQR and z-score methods and interprets what each outlier represents.
• Pivot Table BuilderBuilds a pivot table specification from a described dataset.
• Qualitative CodingProduces a qualitative coding scheme for interview transcripts or open-text survey responses.
• Regression GuideApplies regression analysis to a business question.
• Report FormattingProduces a data report structure with executive summary format, findings section template with chart-plus-interpretation pairs and methodology note.
• Research SynthesisSynthesizes multiple sources into a structured research summary: source quality assessment, finding extraction and theme identification.
• Segmentation DesignDesigns a customer or user segmentation by selecting variables, defining the segmentation method (RFM, behavioral, demographic and needs-based).
• Sentiment Analysis GuideDesigns a sentiment analysis approach for text data by selecting the method (lexicon-based vs. model-based) and defining sentiment categories.
• Spreadsheet AutomationDesigns spreadsheet automation using built-in features including named ranges, drop-down validation lists and INDIRECT references for dynamic ranges.
• Spreadsheet Data CleaningProduces spreadsheet-native data cleaning formulas and operations.
• Spreadsheet Model AuditAudits a spreadsheet model for quality and reliability.
• Streaming Data ArchitectureDesigns a streaming data architecture defining event schema, producer configuration, consumer processing logic and state management approach.
• Survey DesignProduces a complete survey with question set in recommended order, question type selection (Likert, multiple choice, open text and matrix) with rationale.
• Text Mining ProtocolProduces a text mining protocol: defines the corpus, selects the analysis type (frequency, collocation, topic modeling and named entity extraction).