Anthropic Skills

Guidelines for structuring and documenting Jupyter notebooks for reproducibility and clarity.

[DevOps & Infra] Use when optimizing database queries, indexes, N+1 problems, slow queries, or analyzing query performance. Triggers on keywords like "slow query", "N+1", "index", "query optimization", "database performance", "eager loading".

Creates Mermaid and ASCII diagrams for flowcharts, architecture, ERDs, state machines, mindmaps, and more. Use when user mentions diagram, flowchart, mermaid, ASCII diagram, text diagram, terminal diagram, visualize, C4, mindmap, architecture diagram, sequence diagram, ERD, or needs visual documentation.

Protocol-driven analysis executor. The consuming agent discovers relevant protocols, composes a prompt, and calls this tool.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

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.

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

Master AI-powered natural language data exploration with Lumen AI. Use this skill when building conversational data analysis interfaces, enabling natural language queries to databases, creating custom AI agents for domain-specific analytics, implementing RAG with document context, or deploying self-service analytics with LLM-generated SQL and visualizations.

Master declarative, no-code data dashboards with Lumen YAML specifications. Use this skill when building standard data exploration dashboards, connecting multiple data sources (files, databases, APIs), creating interactive filters and cross-filtering, designing responsive layouts with indicators and charts, or enabling rapid dashboard prototyping without writing code.

Work with labeled multidimensional arrays for scientific data analysis using Xarray. Use when handling climate data, satellite imagery, oceanographic data, or any multidimensional datasets with coordinates and metadata. Ideal for NetCDF/HDF5 files, time series analysis, and large datasets requiring lazy loading with Dask.

Run Ralph calibration checks to analyze intention drift, technical quality, and self-improvement opportunities. Use when user asks to "ralph calibrate", "check drift", "analyze sessions", or needs to verify work alignment.

Perform GO and KEGG functional enrichment using HOMER from genomic regions (BED/narrowPeak/broadPeak) or gene lists, and produce R-based barplot/dotplot visualizations. Use this skill when you want to perform GO and KEGG functional enrichment using HOMER from genomic regions or just want to link genomic region to genes.

Use when analyzing code structure, researching codebase, investigating architecture, exploring dependencies, creating reports, auditing code quality, or when asked to проанализировать, исследовать, изучить, составить отчёт, провести аудит - provides structured approach for exploration tasks without immediate code changes

This skill should be used when the user asks to \"start a data science project\", \"brainstorm analysis\", \"plan a data analysis\", or wants to clarify analysis requirements. REQUIRED Phase 1 of /ds workflow. Uses Socratic questioning to clarify goals, data sources, and constraints.

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or requests market sizing analysis for a startup or business opportunity.

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Advanced database design and administration for PostgreSQL, MongoDB, and Redis. Use when designing schemas, optimizing queries, managing database performance, or implementing data patterns.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Track product analytics and user behavior with Mixpanel's event-based platform.

Analyze product analytics with Amplitude's digital analytics platform.

Common patterns and workflows for Azure DevOps and database queries. Use to avoid inefficient searches and tangents.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Generate structured comparisons and decision matrices across analyzed frameworks. Use when (1) comparing multiple frameworks or approaches side-by-side, (2) making architectural decisions between alternatives, (3) creating best-of-breed selection documentation, (4) synthesizing findings from multiple analysis skills into actionable decisions, or (5) producing recommendation reports for technical stakeholders.

Data quality testing with dbt tests, Great Expectations, and monitoring.

Documents HOW code works with surgical precision - traces data flow, explains implementation details, provides file:line references. Purely documentarian, no critiques or suggestions for improvement.

Extracts high-value insights from research documents, RCAs, design docs, and memos - filters aggressively to return only actionable information. Research equivalent of analyzing-implementations skill.

Comprehensive Excel spreadsheet creation, editing, and analysis with support for formulas, formatting, charts, data analysis, and visualization. Use when working with .xlsx, .xlsm, .csv files for: (1) Creating spreadsheets with formulas and formatting, (2) Reading/analyzing data, (3) Modifying existing spreadsheets while preserving formulas, (4) Creating charts and visualizations, (5) Data transformation and analysis, (6) Multi-worksheet operations

Expert SQL query writing, optimization, and database schema design with support for PostgreSQL, MySQL, SQLite, and SQL Server. Use when working with databases for: (1) Writing complex SQL queries with joins, subqueries, and window functions, (2) Optimizing slow queries and analyzing execution plans, (3) Designing database schemas with proper normalization, (4) Creating indexes and improving query performance, (5) Writing migrations and handling schema changes, (6) Debugging SQL errors and query

Professional data visualization creation using D3.js with support for interactive charts, custom visualizations, animations, and responsive design. Use for: (1) Creating custom interactive charts, (2) Building dashboards, (3) Network/graph visualizations, (4) Geographic data mapping, (5) Time series analysis, (6) Real-time data visualization, (7) Complex multi-dimensional data displays