Provides expertise in efficient CSV file processing, data cleaning, and transformation. Handles large files, encoding issues, malformed data, and performance optimization for tabular data workflows.
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 404kidwiz/claude-supercode-skills --skill csv-data-wranglerCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncsv-data-wranglerExecute the skills CLI command in your project's root directory to begin installation:
epx add 404kidwiz/claude-supercode-skills --skill csv-data-wranglerFetches csv-data-wrangler from 404kidwiz/claude-supercode-skills 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/csv-data-wrangler
Restart Cursor to activate csv-data-wrangler. Access via /csv-data-wranglerin 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 csv-data-wrangler for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
csv-data-wrangler has been reliable in day-to-day use. Documentation quality is above average for community skills.
csv-data-wrangler reduced setup friction for our internal harness; good balance of opinion and flexibility.
csv-data-wrangler has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in csv-data-wrangler — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: csv-data-wrangler is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for csv-data-wrangler matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: csv-data-wrangler is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in csv-data-wrangler — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for csv-data-wrangler matched our evaluation — installs cleanly and behaves as described in the markdown.
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