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.
Works with
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:
Fetches 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:
Restart Cursor to activate csv-data-wrangler. Access via /csv-data-wrangler in 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.
Submit your Claude Code skill and start earning
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
2
total installs
2
this week
75
GitHub stars
0
upvotes
Run in your terminal
2
installs
2
this week
75
stars
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.
Invoke this skill when:
Do NOT invoke when:
Tool Selection by File Size:
├── < 100MB → pandas
├── 100MB - 1GB → pandas with chunking or polars
├── 1GB - 10GB → DuckDB or polars
├── > 10GB → DuckDB, Spark, or streaming
└── Quick exploration → csvkit or xsv CLI
Processing Type:
├── SQL-like queries → DuckDB
├── Complex transforms → pandas/polars
├── Simple filtering → csvkit/xsv
└── Streaming → Python csv module
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| Loading all to memory | OOM on large files | Use chunking or streaming |
| Guessing encoding | Corrupted characters | Detect with chardet first |
| Ignoring quoting | Broken field parsing | Use proper CSV parser |
| No validation | Silent data corruption | Validate row/column counts |
| Manual string splitting | Breaks on edge cases | Use csv module or pandas |
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
pproenca/dot-skills
mattpocock/skills
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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