Anthropic Skills

Generate structured narrative text visualizations from data using T8 Syntax. Use when users want to create data interpretation reports, summaries, or structured articles with semantic entity annotations. T8 is designed for unstructured data visualization where T stands for Text and 8 represents a byte of 8 bits, symbolizing deep insights beneath the text.

This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

Create beautiful infographics based on given text content. Use when users request to create infographics.

Create a lightweight summary of experiment results from a completed (fine-tuned and evaluated) experiment. Use after run-experiment to capture key metrics from the experiment in textual form.

Review and improve Databricks SQL queries for correctness, readability, and performance (joins, filters, aggregations, partition pruning). Use when someone pastes a SQL query, asks why it is slow, or requests a rewrite/optimization in Databricks SQL.

Generate structured decision-making tools — step-by-step guides, bias checkers, scenario explorers, and interactive dashboards. Use when facing significant choices requiring systematic analysis. Supports multiple cognitive styles and output formats.

Expert guidance for explaining project features. Use this skill when you need to provide a comprehensive explanation of how a specific feature works, including summaries, deep dives, usage examples, and sequence/workflow diagrams. This skill has references directory which contains additional instructions `checklist.md`, `example-output.md` and `explanation-template.md` that MUST be used for every analysis.

Helps with data stuff

For data

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데이터 분석 전문가. pandas, numpy, 시각화, 통계 분석 지원.

Financial calculations: loans, investments, NPV/IRR, retirement planning, Monte Carlo simulations. Generates tables, charts, and exportable reports.

Generate professional PDF/HTML reports with charts, tables, and narrative from data. Supports templates, branding, and automated report generation.

Analyze text sentiment (positive/negative/neutral) with confidence scores, emotion detection, and visualization. Supports single text, CSV batch, and trend analysis.

Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.

Generate pivot tables from CSV/Excel with aggregations, filters, and automatic chart creation.

Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.

Calculate ROI for marketing campaigns, investments, and business decisions. Includes break-even analysis, payback period, and comparative ROI.

Extract tables from PDFs and images to CSV or Excel. Support for scanned documents with OCR, multi-page PDFs, and complex table structures.

Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.

Use when asked to create publication-ready scientific figures, charts for research papers, or academic visualizations.

Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.

Detect anomalies and outliers in datasets using statistical and ML methods. Use for data cleaning, fraud detection, or quality control analysis.

Create interactive Sankey diagrams for flow visualization from CSV, DataFrame, or dict data. Supports node/link styling and HTML/PNG/SVG export.

Calculate statistical significance for A/B tests. Sample size estimation, power analysis, and conversion rate comparisons with confidence intervals.

Statistical analysis strategies and data exploration techniques

Generate formatted expense reports from receipt data or CSV. Create professional PDF reports with categorization, totals, and approval workflows.

Analyze survey responses with Likert scale analysis, cross-tabulations, sentiment scoring, and frequency distributions with visualizations.

Find and visualize correlations between variables in datasets. Use for data exploration, feature selection, or identifying relationships between columns.

Generate organizational hierarchy charts from CSV, JSON, or nested data. Supports multiple layouts, department coloring, and PNG/SVG/PDF export.

Decompose time series into trend, seasonal, and residual components. Use for forecasting, pattern analysis, and seasonality detection.

Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.

Analyze personal or business expenses from CSV/Excel. Categorize spending, identify trends, compare periods, and get savings recommendations.

Use when asked to visualize sales territories, coverage areas, service regions, or geographic boundaries on interactive maps.

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

Use this skill when designing database schemas for relational (SQL) or document (NoSQL) databases. Provides normalization guidelines, indexing strategies, migration patterns, and performance optimization techniques. Ensures scalable, maintainable, and performant data models.

This skill calculates key financial ratios and metrics from financial statement data for investment analysis