Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL.
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
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
Submit your Claude Code skill and start earning
epx add supercent-io/skills-template --skill data-analysisCompatible with
Supported agents
Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries
Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables
Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn
Generates structured markdown reports with datase
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:
epx add supercent-io/skills-template --skill data-analysisFetches data-analysis from supercent-io/skills-template 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:
.cursor/skills/data-analysis
Restart Cursor to activate data-analysis. Access via /data-analysisin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
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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.
Useful defaults in data-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for data-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
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.
data-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend data-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
data-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
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