Use when applying data science best practices.
Scanned 9/10/2026
Install to Claude Code
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---
name: data-science-best-practices
description: "Use when applying data science best practices."
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [ds, data, data-science-fundamentals]
related_skills: [general]
---
## Overview
Do data science.
## When to Use
- Design and implement
- Apply best practices
- Optimize performance
- Troubleshoot issues
## Key Approaches
1. Define technical requirements
2. Choose appropriate tools
3. Implement modular design
4. Test thoroughly at scale
5. Document decisions
6. Monitor results
## Common Pitfalls
1. Ignoring constraints
2. Skipping standards
3. Poor alignment
4. Inadequate testing
5. No documentation
6. Over-engineering
7. No rollback plan
8. No monitoring
9. No scalability
10. No validation
## Verification Checklist
- [ ] Requirements validated
- [ ] Standards applied
- [ ] Design reviewed
- [ ] Tests defined
- [ ] Docs complete
- [ ] Monitoring configured
- [ ] Rollback planned
- [ ] Security checked
- [ ] Deploy verified
- [ ] Stakeholder approved
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