7 Claude Skills for Data Analysts That Speed Up the Analysis Workflow

The SQL query takes twenty minutes. Profiling the new data source you haven’t seen before takes two hours. Explaining why the join is producing duplicates takes another hour. Building the report scaffold — the same scaffold you built three weeks ago for a different stakeholder in a slightly different format — takes forty-five minutes. By the time you sit down to interpret what the data actually means, most of the day is gone.

Agent Skills push Claude toward handling the setup and structure layer with a consistent, expert approach so you can focus on what the data actually means. Install a skill once and Claude runs that task the same way every time — the same SQL patterns, the same data quality checks, the same chart conventions — without you having to re-specify the approach each session.

The seven skills below cover the full analytical workflow from initial data exploration through to visualisation, dashboards, and experiment design. They work best with Claude Code, where direct file access and shell execution make the data workflows actually usable rather than theoretical.

The skills

1. Data Analyze

Most analytical questions have a clear answer buried somewhere in the data. Getting to it requires pulling the right query, building the right comparison, isolating the right segment, and then communicating the finding in a way that’s useful to the person asking. Doing all of that from a blank prompt every time is slower than it should be.

The Data Analyze skill gives Claude a structured approach to answering data questions: quick metric lookups, trend decomposition, segment comparisons, anomaly investigation, and the kind of full analyses that end in a stakeholder-ready summary with an explicit finding and recommended action.

Trigger it with the question you’re trying to answer and paste in your data or query results. Works equally well for quick sanity checks and for structured analytical deep-dives — the skill adapts the depth to the question.

npx skills add anthropics/knowledge-work-plugins --skill data

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2. Data SQL Queries

Writing SQL well requires knowing the dialect, understanding the schema, choosing the right join type, and structuring the query for readability and performance — not just correctness. Most analysts write working SQL quickly, but performant and readable SQL takes longer, especially across dialects.

The Data SQL Queries skill gives Claude a structured approach to writing cross-dialect SQL: SELECT, JOIN, aggregation, window functions, CTEs, and complex analytical queries optimised for both readability and execution performance. It handles the boilerplate so you can describe what you need in plain English and get production-ready SQL back.

Use it for complex queries where you know what you want but the SQL is non-trivial to write, for translating queries between dialects (Postgres to BigQuery, MySQL to Snowflake), or for reviewing and optimising queries that are running slowly.

npx skills add anthropics/knowledge-work-plugins --skill data

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3. Data Build Dashboard

Sharing analysis with stakeholders who don’t have BI tool access is a recurring problem. Exporting a CSV and attaching it to a Slack message isn’t a dashboard. A static screenshot doesn’t answer follow-up questions. Building a proper interactive report without a BI tool takes longer than it should.

The Data Build Dashboard skill gives Claude a structured approach to building interactive HTML dashboards — charts, filters, and tables assembled into a self-contained browser-openable file that anyone can view without installing anything. It covers chart selection, layout, and interactivity decisions automatically.

Use it when you need to share an executive overview with stakeholders who don’t have BI access, when you want to turn query results into a shareable report quickly, or when you need a team monitoring snapshot for a project that doesn’t warrant a full BI implementation.

npx skills add anthropics/knowledge-work-plugins --skill data

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4. Data Create Viz

Choosing the right chart type, scaling the axes correctly, and making the visual actually communicate the finding — rather than just displaying the data — is a design problem as much as an analytical one. Most analysts can build a chart quickly. Building one that makes the insight immediately obvious takes more thought.

The Data Create Viz skill gives Claude a structured approach to creating publication-quality visualisations with Python: selecting the chart type appropriate for the data and the story you’re telling, generating plots for reports and presentations, building interactive charts with hover and zoom for exploratory views, and applying formatting conventions that make the output look polished rather than procedurally generated.

Use it when producing charts for a stakeholder presentation, for a report that will be shared outside the data team, or when the visualisation needs to communicate a finding clearly rather than just displaying the numbers.

npx skills add anthropics/knowledge-work-plugins --skill data

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5. Data Explore Data

Starting a new dataset without understanding its structure is a reliable way to build analysis on a shaky foundation. What’s the grain of the table? What’s the null rate on the columns you plan to use? Are there duplicates? What does the distribution of the key metric look like? These questions take time to answer manually on every new dataset.

The Data Explore Data skill gives Claude a structured approach to profiling a new dataset: summary statistics, data types, missing value rates, outlier detection, cardinality checks, and data quality observations — all assembled into a structured profile you can use to decide whether the data is ready for analysis or needs cleaning first.

Use it at the start of any analysis involving a dataset you haven’t worked with before. The exploration takes a few minutes and consistently surfaces issues that would otherwise show up later, at a point where they’re more expensive to fix.

npx skills add anthropics/knowledge-work-plugins --skill data

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6. Data Validate Data

Data pipelines drift. Tables that were clean six months ago have accumulated null rows from a schema change, duplicate records from a join issue, or values outside the expected range from an upstream bug. Running analysis on data you haven’t validated is a quiet risk — the results look plausible until someone finds the error.

The Data Validate Data skill gives Claude a structured approach to data quality validation: integrity checks, schema drift detection, duplicate row identification, null rate analysis, range violation flagging, and referential integrity checks across related tables. The output is a validation report that tells you exactly what’s wrong and where.

Use it before any analysis that feeds a report, a dashboard, or a business decision. Especially important when the data source is external, when the pipeline has recently changed, or when you’re running a one-time analysis where you won’t have a chance to catch errors after the fact.

npx skills add anthropics/knowledge-work-plugins --skill data

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7. A/B Test Setup

Data teams are often the ones who catch experiment design problems — after the test has been running for three weeks and the results are ambiguous. Underpowered samples, incorrect primary metric selection, multiple comparison issues, starting analysis before the planned end date: the methodological mistakes that make a test inconclusive are all preventable at design time.

The A/B Test Setup skill gives Claude a structured approach to experiment design: hypothesis formulation, sample size calculation for the required statistical power, variant design review, primary and guardrail metric definition, and the pre-registered interpretation framework that prevents motivated reasoning during results analysis.

Use it before a test is built, not while reviewing the results. The skill is most valuable at the point where fixing design problems costs nothing — and least valuable after six weeks of data collection have already produced ambiguous output.

npx skills add alirezarezvani/claude-skills --skill marketing-skill/ab-test-setup

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How these skills chain together

Here’s how these skills flow through a typical analytical project — from a new dataset landing to a stakeholder report going out.

Dataset arrives: Use Data Explore Data first — profile the dataset before writing a single query. Understand the grain, the nulls, the outliers.

Before analysis: Use Data Validate Data to check integrity. Catch issues before they become errors in the output.

Writing queries: Use Data SQL Queries to build the analytical queries. Describe the question in plain English; get production-ready SQL back.

Running the analysis: Use Data Analyze to structure the findings — trend decomposition, segment comparison, the narrative that connects the numbers to the question.

Experiment design: Use A/B Test Setup if the analysis leads to a hypothesis worth testing. Design the experiment correctly before building it.

Output: Use Data Create Viz for charts going into reports or presentations. Use Data Build Dashboard if the audience needs an interactive view they can explore themselves.

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How to install

New to Agent Skills? Install with one command or download and paste manually into Claude.ai.

→ Full install guide

Browse all data skills → /audiences/data