Automated analysis of 200+ scientific data formats with format-specific insights and quality assessment.
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 davila7/claude-code-templates --skill exploratory-data-analysisCompatible with
Supported agents
Supports six major scientific domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and metabolomics, with 60+ formats per category
Auto-detects file type and generates detailed markdown reports including metadata extraction, statistical summaries, data quality metrics, and visualization recommendations
Includes reference documentation for each format covering
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionexploratory-data-analysisExecute the skills CLI command in your project's root directory to begin installation:
epx add davila7/claude-code-templates --skill exploratory-data-analysisFetches exploratory-data-analysis from davila7/claude-code-templates 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/exploratory-data-analysis
Restart Cursor to activate exploratory-data-analysis. Access via /exploratory-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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Useful defaults in exploratory-data-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for exploratory-data-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
exploratory-data-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend exploratory-data-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
exploratory-data-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
exploratory-data-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: exploratory-data-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added exploratory-data-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
exploratory-data-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
exploratory-data-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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