Data Analysis Claude Skills
2 409 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 952 tokens or less — that is what one costs your context window when the agent loads it. 549 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 386 copies of these same skills sitting in other people's repositories — counted once here, not 386 times.
2 409 unique 443 authors 5 updated this month 241 from vendors
Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.
Query Google Data Commons for public statistical data aggregated from global sources, resolving geographic entities and pulling time-series statistics. Use when working with demographic data, economic indicators, health statistics, or environmental data — population counts, GDP figures, unemployment rates, disease prevalence — or when resolving places to DCIDs and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.
Query the openFDA API for drugs, medical devices, adverse event reports, recalls, regulatory submissions (510k, PMA), and substance identification (UNII). Use when searching FDA safety data, pharmacovigilance and adverse-event signals, device clearances, drug labels, or recall records for regulatory data analysis and safety research. Part of the AlterLab Academic Skills suite.
Query the NHGRI-EBI GWAS Catalog REST API for SNP-trait associations, retrieving variants by rs ID, disease/trait, or gene along with p-values and summary statistics. Use when investigating genome-wide association study hits, mapping a SNP or rsID to traits, building polygenic risk scores, or doing genetic epidemiology lookups. Part of the AlterLab Academic Skills suite.
Processes astronomy and astrophysics data with the Astropy Python library — celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, and world coordinate systems (WCS). Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or general astronomical data analysis. Part of the AlterLab Academic Skills suite.
Queries the U.S. Treasury Fiscal Data API across 54 datasets and 182 data tables (no API key required) for federal financial data on national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics. Part of the AlterLab Academic Skills suite.
Enforces pre-registration discipline with the Iron Law NO DATA ANALYSIS WITHOUT A PRE-REGISTERED ANALYSIS PLAN FIRST, a spirit-vs-letter line, an Excuse-vs-Reality rationalization table, and a Red-Flags-STOP list (HARKing, optional stopping, post-hoc covariates, outlier-dropping, test-shopping). Runs a PLAN/COLLECT/CONFIRM/EXPLORE workflow that freezes hypotheses, tests, exclusions, and stopping rules before data, then forces unplanned findings to be labeled exploratory (their p-values lose confirmatory status, per COS confirmatory/exploratory model). Orchestrates, not replaces, alterlab-open-science (OSF/AsPredicted registration), alterlab-statistical-analysis (test selection, assumptions), and alterlab-scientific-thinking (bias grading). Use when analyzing data without a frozen plan, switching the primary outcome or adding covariates after seeing results, weighing early stopping, dropping outliers post-hoc, pre-registering a study, or rationalizing deviation. Part of the AlterLab Academic Skills suite.
Gates measurement quality before a scale or instrument is trusted — checks that each construct is defined and operationalized, that reliability is evidenced with McDonald omega (not Cronbach alpha alone, which assumes tau-equivalence and is only a lower bound), that reliability is not confused with validity (content, criterion, convergent/discriminant construct validity), and that measurement invariance is tested before comparing groups. Use when asking whether a scale or survey instrument is valid, reporting a Cronbach alpha, building or adopting a multi-item measure, or comparing a latent construct across groups. For designing the questionnaire items prefer alterlab-survey-design; for running the confirmatory factor analysis prefer alterlab-sem-psychometrics; to execute basic statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
Builds INTERACTIVE charts with the Plotly Python library (plotly.express / graph_objects) — hover tooltips, zoom/pan, animations, rangesliders, 3D rotation, and standalone HTML/web-embeddable output. Use when a chart must be interactive or web-embedded, for dashboards (incl. Dash), exploratory data analysis, or rotatable 3D plots. For static publication figures defer to alterlab-matplotlib; for static statistical charts (heatmaps, distributions) defer to alterlab-seaborn; for diagrams/schematics defer to alterlab-scientific-viz. Part of the AlterLab Academic Skills suite.
Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.
Simple Pomodoro timer for focused work sessions with session tracking and productivity analytics. Use when users request focus timers, ask about productivity patterns, or want to track work sessions over time. Demonstrates the System Skill Pattern (CLI + SKILL.md + Database).
> 🤖 Data Science & AI/ML skill suite derived from BehiSecc/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
| Build human adjudication / hand-labeling sheets from LLM-pipeline data without rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard multi-line CSV mangling, ruling dropdowns, companion text files.
Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.
>- Use this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.
This skill should be used when analyzing weekly price charts for Indian stocks (NSE/BSE), indices (Nifty 50, Bank Nifty, Sensex), or any other instrument. Use this skill when the user provides chart images and requests technical analysis, trend identification, support/resistance levels, scenario planning, or probability assessments based purely on chart data without consideration of news or fundamental factors.
> Think and deliver like a management consultant from McKinsey, BCG, or Bain. hypothesis-driven decomposition, (2) Run strategy analysis with professional McKinsey verdict-first, BCG framework-first, or Bain decision-first, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets.
| (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
> 🤖 Data Science & AI/ML skill suite derived from alirezarezvani/claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.
Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.
Design or redesign a MotherDuck Dive as a responsive, reusable analytics interface. Use when a Dive must be mobile-friendly from the start, support light and dark modes, reserve space for filters, use restrained Power BI-style information design, embed small charts inside metric components, or work across customers without one-off layout changes.
Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.
Design or redesign a MotherDuck Dive as a responsive, reusable analytics interface. Use when a Dive must be mobile-friendly from the start, support light and dark modes, reserve space for filters, use restrained Power BI-style information design, embed small charts inside metric components, or work across customers without one-off layout changes.
Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.
> Create, list, download, update, and delete CrowdStrike Falcon Next-Gen SIEM lookup files. Upload CSV or JSON files for use with the match() function in CrowdStrike Query Language (CQL) queries. Use this skill when asked to manage lookup files, upload CSV data to CrowdStrike, create reference tables for SIEM queries, or work with Falcon Next-Gen SIEM lookup file operations.
AI Native 产品方法论——AI Native SaaS 行业案例模板 Skill。 用户提供 SaaS 产品场景和业务背景，Skill 自动执行全链路方法论： 方向定界 → 试验展开 → 系统构建 → 审计放行 → 生产运行，输出 AI Native 化改造方案。 基于《AI Native 产品方法论》第21章 AI Native SaaS 案例，适用于数据分析、业务洞察、决策支持场景。 '
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans with rollback strategies.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Build a Community Pack: strategy, platform plan, ambassador program, governance, metrics.
Produce a Cross-Functional Collaboration Pack (charter, stakeholder map, roles contract, decision log).
Stand up a Design Engineering practice: charter, prototype-to-production workflow, component delivery plan.
Create a Platform Strategy Pack (charter, interface map, ecosystem model, governance, metrics).