Data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib.
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 pluginagentmarketplace/custom-plugin-python --skill pandas-data-analysisCompatible with
Supported agents
Covers DataFrame and Series creation, indexing, filtering, and type conversions for structured data handling
Includes data cleaning techniques: missing value handling, deduplication, string operations, and date/time parsing
Provides GroupBy aggregation, pivot tables, multi-level indexing, and window functions for exploratory analysis
Integrates Matplotlib and Seaborn for statistical plotting, trend
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpandas-data-analysisExecute the skills CLI command in your project's root directory to begin installation:
epx add pluginagentmarketplace/custom-plugin-python --skill pandas-data-analysisFetches pandas-data-analysis from pluginagentmarketplace/custom-plugin-python 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/pandas-data-analysis
Restart Cursor to activate pandas-data-analysis. Access via /pandas-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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I recommend pandas-data-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for pandas-data-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: pandas-data-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: pandas-data-analysis is focused, and the summary matches what you get after install.
pandas-data-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added pandas-data-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
pandas-data-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend pandas-data-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in pandas-data-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
pandas-data-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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