Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks.
Run in your terminal
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondata_analysisExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/artificialanalysis/stirrup --skill data_analysisFetches data_analysis from artificialanalysis/stirrup and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate data_analysis. Access via /data_analysisin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Package manager
npx skills add https://github.com/artificialanalysis/stirrup --skill data_analysisWorks with
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total installs
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this week
343
GitHub stars
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upvotes
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: data_analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in data_analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
data_analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
data_analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in data_analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
data_analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: data_analysis is focused, and the summary matches what you get after install.
I recommend data_analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: data_analysis is focused, and the summary matches what you get after install.
Useful defaults in data_analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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