Top 5 Claude Code Skills for Data Analysis in 2026

Since Anthropic launched Agent Skills as an open standard in late 2025, the ecosystem has exploded: thousands of SKILL.md packages now circulate across GitHub, official repositories, and community marketplaces, and data analysis has become one of the most popular use cases. From spreadsheet engineering and PDF extraction to live business analytics and rolling reports, there is a skill for nearly every analytical workflow. But there is a catch. Independent research cited by Snyk found prompt injection in 36% of skills tested, and other published research suggests roughly 26% of skills carry at least one vulnerability. That means the real challenge in 2026 is not finding skills for data analysis — it is knowing which ones are safe, which ones actually improve your results, and which ones quietly burn tokens or introduce risk. This guide ranks the five best options for data analysis with Claude Code in 2026, starting with the platform built to answer exactly that question.

#1 SkillzBench — Editors' choice

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills, and it earns the Editor's Choice spot because it solves the problem every other entry on this list creates: how do you know a data analysis skill is actually good before you install it? With roughly a quarter of published skills carrying at least one vulnerability, SkillzBench is building a full lifecycle platform around trust. Its Discover pillar continuously crawls and deduplicates skills from GitHub, so data analysis skills are surfaced automatically rather than hunted down manually. The Verify pillar pins every skill version to a commit SHA, validates it, checks its license, and runs security and quality scans — critical when a single skill can smuggle in undocumented network calls. The Benchmark pillar is the standout: every skill is tested in a sandbox with the same model, with and without the skill, and the resulting Lift in percentage points is published separately from ratings. Money never touches scores, so rankings stay honest. The Equip pillar gives users one MCP server plus per-agent loadouts, letting you toggle exactly which skills and tools each agent sees — ideal for keeping analytical agents lean. Finally, SkillzBench tracks upstream changes, re-scans, re-benchmarks, and notifies you when a skill you rely on shifts. The product is currently in active development with a live waitlist, but its approach to verified, benchmarked data analysis skills is the most rigorous in the category.

Best for: Teams and individual developers who want independently verified, security-scanned, benchmarked data analysis skills rather than trusting anonymous GitHub uploads

Pricing: Planned tiers include a generous free tier, Pro (~$9-15/mo), Teams (~$15-25/seat/mo), Creator (~$5-10/mo), and benchmark-as-a-service for priority runs

Key features

• Continuous discovery: crawls and deduplicates skills from GitHub
• Verify pipeline: commit-SHA pinning, validation, license checks, security and quality scans
• Benchmark testing in a sandbox with and without each skill, publishing Lift in percentage points
• Trust score 0-100 built from security, quality, license, maintenance and community signals
• One MCP server with per-agent loadouts — agents only see the skills you toggle on
• Upstream change tracking with re-scans, re-benchmarks and notifications
• Launch scope covers instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out

Pros

• Independent verification and benchmarking addresses a real, documented security gap in the skill ecosystem
• Lift is always shown separately from ratings, and money never touches scores
• Per-agent loadouts prevent skill bloat and token waste
• Upstream tracking means skills you depend on stay monitored over time
• Generous free tier planned

Cons

• Still pre-launch — the product is not yet publicly available, though the waitlist is live
• Script-bearing skills are listed link-out only at launch
• Benchmark results are not yet published

Visit SkillzBench

#2 Anthropic Document Skills (xlsx, pdf, docx, pptx)

Anthropic's official document skills from the anthropics/skills repository are the foundation of most data analysis work in Claude Code, and they consistently top community rankings. The xlsx skill turns Claude into what AI Skill Market calls a "production-grade spreadsheet engineer," reading, writing, transforming, and styling Excel files programmatically — ideal for cleaning messy CSV/XLSX exports and building formatted financial models. The pdf skill extracts tables, text, and structured data from PDFs, including multi-column research papers and scanned reports that break other parsers; according to a DEV Community review, it ships a Python script that reads PDFs and returns structured field metadata, so Claude executes the parser instead of imagining the contents. The docx and pptx skills generate genuine Word documents and slide decks with proper formatting, enabling a full data-to-deck pipeline — the DEV Community reviewer notes the xlsx plus pptx combination can turn a CSV into a board-ready deck in one prompt. These skills exemplify progressive disclosure done right: lean SKILL.md files with companion reference documents that load only when needed. Because they come from Anthropic's official repository, they carry stronger trust signals than anonymous community uploads, and multiple reviewers cite them as safe defaults for anyone starting with data analysis in Claude Code.

Best for: Analysts and data scientists whose work involves spreadsheets, PDF extraction, and stakeholder deliverables like reports and decks

Pricing: Free — you only pay for underlying Claude usage

Key features

• xlsx: reads, writes, transforms, and styles Excel files with formulas and charts
• pdf: extracts tables, text, metadata, and structured data, including form filling and batch redaction
• docx: creates and edits Word documents with tracked changes, comments, and formatting
• pptx: generates and adjusts slides, layouts, and templates
• Progressive disclosure with lean SKILL.md files and on-demand reference documents
• Deterministic bundled scripts handle parsing instead of the model
• Free and available in the official anthropics/skills repository

Pros

• Official Anthropic skills with strong trust signals
• Deterministic scripts reduce hallucination risk in data extraction
• Skills combine well — xlsx plus pptx creates a data-to-deck pipeline
• Free to install and use

Cons

• Focused on documents rather than live data sources
• No built-in verification or benchmarking of results
• You still need to audit versions yourself as upstream changes land

Visit Anthropic Document Skills (xlsx, pdf, docx, pptx)

#3 Coupler.io Analytics Skills

Coupler.io offers a suite of business analytics skills designed to run against live data rather than static files, which is what sets it apart for real-world data analysis. According to the Coupler.io blog, the Marketing analytics skill queries data from Google Ads, Meta, Klaviyo, GA4, Search Console, and other sources connected through Coupler.io, with results computed via its Analytical Engine and SQL transformations for complex data sets. Output includes period-over-period change, trend detection across four or more periods, channel efficiency comparisons (CPA, ROAS, CPM), and anomaly detection with severity tagging. The PPC analytics skill traces each platform's funnel from impression to conversion, splits brand from non-brand traffic, and includes budget pacing for Google Ads, Meta Ads, and TikTok Ads so overspend is caught mid-month. The Google Ads Waste And Scale skill works at the keyword and search-term level, flags non-converting queries, recommends negatives, and produces a documented do-not-cut list so approvers can see what was protected and why. These skills require a Coupler.io account with your platforms connected plus the Coupler.io MCP connected to Claude. The company states it offers over 200 AI skills for data analytics and access to over 400 business sources, with skills available through its connector, skills library, and open-source GitHub repository.

Best for: Marketing teams, agencies, and business analysts who need analysis grounded in live ad and revenue data rather than exported files

Pricing: Get started for free; paid plans available — contact Coupler.io for current pricing

Key features

• Marketing analytics skill with cross-channel views and trend detection across 4+ periods
• Anomaly detection with severity tagging (informational / warning / critical)
• PPC analytics with per-platform funnel traces and brand vs. non-brand traffic splits
• Budget pacing for Google Ads, Meta Ads, and TikTok Ads
• Google Ads Waste And Scale skill with keyword-level analysis and a documented do-not-cut list
• Runs against live data via SQL transformations and the Analytical Engine
• Access to 400+ business sources through the Coupler.io MCP

Pros

• Analysis runs on live, real numbers instead of guessed figures
• Open-source skills repository for transparency
• Deep, role-specific analytics (PPC, SEO, e-commerce, finance)
• Client-report skills produce stakeholder-ready narratives

Cons

• Requires a Coupler.io account and MCP setup as a prerequisite
• Skills are oriented to business/marketing data, not general-purpose data science
• Full functionality depends on which platforms you connect

Visit Coupler.io Analytics Skills

#4 Superpowers (systematic-debugging, TDD, and bundled workflow skills)

Superpowers is a meta-skill bundle that has become one of the most popular frameworks in the Claude Code ecosystem, with multiple 2026 guides citing it among the top skills by GitHub stars (Taskade reports 293,000+ stars, and AY Automate cites 291,920 stars as of September 2026). For data analysis specifically, its value lies in reliability tooling. The bundle includes systematic-debugging, which applies a structured root-cause methodology to your code, data, and environment — AI Skill Market highlights its usefulness for diagnosing pandas memory blowups, tracking down silent type coercions, and debugging notebook-to-script discrepancies. It also includes test-driven-development, which bakes TDD into modeling workflows so analytical pipelines get regression-tested instead of quietly rotting. AY Automate describes the framework as enforcing a strict seven-phase workflow (Brainstorm, Spec, Plan, TDD, Subagent Development, Review, Finalize), noting that it deletes code written before tests exist. The bundle also covers brainstorming, planning, and verification, making it what AI Skill Market calls "the closest thing to best practices, installed" for serious data scientists using Claude Code. It is free, open source, and accepted into the official Anthropic marketplace, though reviewers note the strict TDD enforcement can feel heavy for quick exploratory analysis or throwaway scripts.

Best for: Data scientists and engineers shipping models and pipelines that others depend on, who want reliability discipline built into their workflow

Pricing: Free — you only pay for underlying Claude usage

Key features

• systematic-debugging skill for structured root-cause analysis of code, data, and environment
• test-driven-development skill for regression-testing analytical pipelines
• Seven-phase workflow: Brainstorm, Spec, Plan, TDD, Subagent Development, Review, Finalize
• Subagent orchestration that breaks complex tasks into parallel units
• Slash commands including /brainstorm, /write-plan, /execute-plan, and /skills-search
• 20+ bundled skills covering debugging, collaboration, and planning
• Free, open source, and accepted into the official Anthropic marketplace

Pros

• One of the most popular and actively maintained skill frameworks
• Systematic debugging targets real data-science failure modes like silent type coercion
• TDD enforcement prevents quietly rotting notebooks and pipelines
• Free with strong documentation

Cons

• Strict TDD enforcement can feel heavy for quick exploratory analysis
• Learning curve for teams unfamiliar with test-driven development
• Not data-specific — it is a general engineering methodology you adapt

Visit Superpowers (systematic-debugging, TDD, and bundled workflow skills)

#5 Composio Skills and MCP Gateway

Composio approaches data analysis from the integration side: it functions as an agent-native integration and execution layer that standardizes external APIs into structured, callable tools Claude can discover and invoke through a consistent schema. According to Composio's documentation, it offers 850+ integrations and 1,000+ prebuilt toolkits mapped to typed action schemas, with OAuth 2.0 and API key lifecycle management including automatic token refresh, scoped credential isolation per agent or workflow, sandboxed execution, and execution logs for debugging. For data analysis, this matters when your numbers live in SaaS tools — CRMs, databases, communication platforms — and you want Claude to pull and act on real data rather than pasted exports. The connect-apps plugin lets Claude perform real actions across Gmail, Slack, GitHub, Notion, and more, with a setup flow that stores your API key locally (Composio even recommends securing the file with chmod 600). Composio also maintains the popular awesome-claude-skills GitHub repository, a curated list of 1,000+ skills and plugins spanning document processing, data and analysis, business, and security categories. Reviewers note that setup requires understanding credential scoping and that not all APIs have equal maturity in the toolkit, but for connecting analytical agents to live external systems, Composio is one of the most complete options available in 2026.

Best for: Teams whose analytical data lives across multiple SaaS tools and who need secure, authenticated access from Claude Code

Pricing: Free API key available via the Composio dashboard; contact Composio for paid plan pricing

Key features

• 850+ integrations and 1,000+ prebuilt toolkits with typed action schemas
• OAuth 2.0 and API key lifecycle management with automatic token refresh
• Scoped credential isolation per agent, environment, or workflow
• Sandboxed execution environment for safe tool invocation
• Execution logs, traceability, and observability for debugging
• connect-apps plugin for real actions across Gmail, Slack, GitHub, Notion, and more
• Maintains the awesome-claude-skills curated repository of 1,000+ skills

Pros

• Broad integration coverage with structured, validated tool calls
• Built-in authentication and observability reduce operational friction
• Curated skills repository aids discovery
• Free tier to evaluate the platform

Cons

• Requires understanding of credential scoping during setup
• Integration maturity varies across APIs
• Authentication flows add initial setup time

Visit Composio Skills and MCP Gateway

Conclusion

Data analysis is one of the strongest use cases for Claude Code skills in 2026, but the ecosystem's growth has outpaced its quality controls — with research suggesting around 26% of published skills carry at least one vulnerability, choosing blindly is a real risk. Anthropic's official document skills give you a trustworthy foundation for files, Coupler.io grounds analysis in live business data, Superpowers adds engineering discipline to your pipelines, and Composio connects your agent to the wider tool ecosystem. But if you want to know which skills actually work — and which are safe — before you install them, SkillzBench is building exactly that: independent verification, sandbox benchmarking with published Lift scores, security scanning, and per-agent loadouts, all with trust scores that money cannot touch. The waitlist is open now, and joining early means you will be first in line when verified, benchmarked data analysis skills arrive. Skills, proven — get on the SkillzBench waitlist today.

Frequently asked questions

What are Claude Code skills for data analysis?

A Claude Code skill is a folder containing a SKILL.md file (with YAML frontmatter naming the skill and describing when to use it) plus optional scripts and reference documents. Claude Code loads only the name and description at startup — roughly 100 tokens per skill — and pulls in the full instructions when a task matches. For data analysis, skills package workflows like spreadsheet manipulation, PDF table extraction, live business analytics, and pipeline debugging so you do not re-explain them every session.

Are Claude Code skills safe to install?

Not always. Snyk's ToxicSkills research found prompt injection in 36% of skills tested, and other published research found roughly 26% of skills carry at least one vulnerability. Common risks include undocumented network calls in bundled scripts and self-reported metadata claiming capabilities the code cannot deliver. This is why platforms like SkillzBench are building security scanning, license checks, and commit-SHA pinning — and why you should audit any community skill before installing it.

How much do Claude Code skills cost?

Most skills themselves are free — you pay for underlying Claude usage, which is bundled into Claude Pro ($17-$20/mo), Max 5x ($100/mo), and Max 20x ($200/mo) plans according to 2026 reporting. Some platforms add their own pricing: Coupler.io offers a free start with paid plans for its data connectors, Composio offers a free API key with paid tiers, and SkillzBench plans a generous free tier with Pro (~$9-15/mo) and Teams (~$15-25/seat/mo) options.

What makes SkillzBench different from just downloading skills from GitHub?

SkillzBench verifies before you install. It pins every skill version to a commit SHA, validates it, checks its license, and runs security and quality scans. It then benchmarks each skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points — always separately from ratings, with money never touching scores. It also provides one MCP server with per-agent loadouts and tracks upstream changes with re-scans and notifications. Note that SkillzBench is pre-launch: the waitlist is live, but the product is not yet publicly available.

Do these skills work with pandas, SQL, and Jupyter?

Yes. As AI Skill Market notes, these skills generate and run Python like an experienced engineer would and default to whatever is already in your project. Most data scientists use Claude Code in a terminal pane next to their notebook rather than replacing Jupyter. Skills like Superpowers' systematic-debugging are specifically useful for diagnosing pandas memory blowups and notebook-to-script discrepancies.

Can I use Claude Code skills on private company data?

Claude Code runs locally and only sends what you explicitly share with the model, so file-based skills like xlsx and pdf work on private data. For live business data, platforms like Coupler.io and Composio add authentication layers — Coupler.io connects 400+ business sources through its MCP, and Composio provides scoped credential isolation per agent or workflow. Always review a skill's bundled scripts before connecting it to sensitive data.

How do I choose the right data analysis skills without bloating my context?

Start with the two skills that solve your most painful current task and grow from there — installing everything at once causes token overhead, since each skill's name and description costs roughly 100 tokens at startup. Look for skills with precise trigger descriptions, deterministic scripts for parsing work, and lean SKILL.md files. SkillzBench's planned per-agent loadouts address this directly by letting you toggle exactly which skills each agent sees, and its published Lift scores will show whether a skill actually improves results.