The 10 Best Data Science AI Skills for 2026

Data science teams in 2026 are running leaner than ever. Instead of stitching together Jupyter, pandas tutorials, half-broken notebooks, and a quarterly BI license, more teams are reaching for Claude Code skills that drop directly into their workflow. The result: faster exploratory analysis, cleaner reports, fewer context switches, and a whole lot less YAML.

This list covers the 10 best data science AI skills for 2026, picked from the AI Skill Market library. Every one of them is install-ready, free, and works alongside your existing pandas, SQL, and notebook habits. Whether you're running an ad-hoc analysis, building a repeatable model, or shipping a stakeholder report, these skills will save you hours per week.

Selection Criteria

We ranked these 10 skills using the following criteria:

• Real-world utility — does it solve a problem data scientists hit weekly?
• Install simplicity — one command, no babysitting
• Output quality — does it produce code/reports a senior would ship?
• Free and open — no paywall to evaluate
• Composability — works alongside other skills, not in isolation

1. xlsx

The official Anthropic XLSX skill turns Claude into a production-grade spreadsheet engineer. It reads, writes, transforms, and styles Excel files programmatically — perfect for the 80% of "data science" work that's actually spreadsheet munging.

Key use cases:

• Cleaning messy CSV/XLSX exports from clients
• Building formatted financial models
• Generating multi-sheet reports with charts

Install: claude skill install xlsx

Why it made the list: Excel still runs the business. Any data scientist who can't move fluently between pandas and .xlsx is leaving hours on the table.

2. pdf

The PDF skill extracts tables, text, and structured data from PDFs — including the gnarly multi-column research papers and scanned reports that break every other parser.

Key use cases:

• Extracting tables from regulatory filings
• Mining academic PDFs for citations and data
• Converting reports to structured JSON

Install: claude skill install pdf

Why it made the list: Half of the world's "data" is locked in PDFs. This skill is the can opener.

3. docx

The DOCX skill produces and edits Word documents at the same fidelity as a human analyst — including tables, footnotes, and tracked changes. Essential for handing reports back to non-technical stakeholders.

Key use cases:

• Auto-generating quarterly analysis reports
• Producing exec summaries from notebooks
• Updating templated client deliverables

Install: claude skill install docx

Why it made the list: Data work isn't done until someone in a suit reads it. DOCX is how you cross that bridge.

4. pptx

The PPTX skill builds slide decks directly from data and analysis. Pair it with xlsx and you have a full data-to-deck pipeline that runs in one prompt.

Key use cases:

• Auto-generating board decks
• Turning notebooks into presentation-ready slides
• Building investor data rooms

Install: claude skill install pptx

Why it made the list: Slides are the universal exchange format for insight. This skill ends the "now make it pretty" tax.

5. systematic-debugging

Data pipelines fail in dramatic, silent ways. This skill applies a structured root-cause methodology to your code, your data, and your environment — the difference between "it works on my machine" and "it works in production."

Key use cases:

• Diagnosing pandas memory blowups
• Tracking down silent type coercions
• Debugging notebook → script discrepancies

Install: claude skill install systematic-debugging

Why it made the list: Most data science bugs are environment bugs. Systematic-debugging treats the whole stack, not just the cell.

6. test-driven-development

Yes, data scientists should write tests. This skill bakes TDD into your modeling workflow — failing tests first, then minimal code, then refactor. The result: notebooks that don't quietly rot.

Key use cases:

• Validating feature engineering steps
• Locking in model expectations
• Regression testing analytical pipelines

Install: claude skill install test-driven-development

Why it made the list: It's the cheapest reliability upgrade you'll ever make.

7. impeccable

A polishing skill that critiques your output and rewrites it to a higher standard — perfect for analysis writeups, model cards, and Slack updates.

Key use cases:

• Sharpening exec summaries
• Cleaning up README and model documentation
• Editing technical blog posts

Install: claude skill install impeccable

Why it made the list: Communication is half the job. Impeccable raises your floor without raising your effort.

8. last30days

A reporting skill that summarizes any data source — git history, support tickets, sales CRM — into a 30-day rolling report. Drop it into a cron and you have living dashboards without any BI tooling.

Key use cases:

• Weekly business reviews
• Engineering velocity reports
• Customer health summaries

Install: claude skill install last30days

Why it made the list: Every data team gets asked "what happened this month?" Last30days answers it on autopilot.

9. skill-creator

Once you've used a few skills, you'll want to build your own. The skill-creator scaffolds new skills with the right structure, frontmatter, and progressive disclosure — ideal for codifying your team's tribal data knowledge.

Key use cases:

• Wrapping internal data pipelines
• Codifying SQL templates
• Creating reusable analysis playbooks

Install: claude skill install skill-creator

Why it made the list: Your repeatable analyses deserve to be skills, not Slack screenshots.

10. superpowers

A meta-skill bundle that includes systematic-debugging, TDD, brainstorming, planning, and verification all in one. The single best starter pack for a serious data scientist using Claude Code.

Key use cases:

• New project kickoff
• Multi-step analyses
• Reproducible research

Install: claude skill install superpowers

Why it made the list: It's the closest thing to "best practices, installed."

Comparison Table

How to Choose

If you're brand new to AI-augmented data science, install superpowers first — it covers most of your daily needs. Add xlsx, pdf, and docx the moment a stakeholder asks for a deliverable. Layer in systematic-debugging and test-driven-development when you start shipping models that other people depend on. Finally, when you notice yourself repeating the same prompt, reach for skill-creator to lock the workflow in.

Don't try to install all 10 on day one. Pick the two that solve your most painful current task, and grow the stack from there.

FAQ

Q: Do these replace Jupyter? No. They run alongside Jupyter, VS Code, or whatever you already use. Most data scientists use Claude Code in a terminal pane next to their notebook.

Q: Are these skills free? Yes. Every skill in this list is free to install. You only pay for your underlying Claude usage.

Q: Will they work with pandas / polars / DuckDB? Yes. These skills generate and run Python like any senior engineer would, and they default to whatever's already in your project.

Q: Can I use them on private data? Yes — Claude Code runs locally and only sends what you explicitly share with the model.

Q: How do I install all 10 at once? Run claude skill install xlsx pdf docx pptx systematic-debugging test-driven-development impeccable last30days skill-creator superpowers.

Conclusion

The data science stack has changed. In 2026, the best data scientists aren't the ones with the most exotic ML libraries — they're the ones who've automated the boring 80%. These 10 skills do exactly that.

Browse more at /browse, explore the full agents catalog, check out workflows, or submit your own skill if you've built something the community should see.

Related Reading

• 150 AI Specialists You Can Hire in 30 Seconds
• 113 Workflows That Run Your Digital Life
• The MCP Protocol Guide
• The 10 Best Python AI Skills for Developers

Sources

• Claude Code Documentation
• Agency Agents on GitHub
• Clawflows on GitHub
• Anthropic Skills Library