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onewave-ai avatar

Player Comparison Tool

onewave-ai/claude-skills
249 installs244 stars
Summary

This does what it says: compares athletes across different eras and leagues by adjusting for pace of play, rule changes, and statistical context. You feed it two players and it normalizes their stats so you're not comparing a 1960s NBA center to a modern one without accounting for pace differences. The real value is that it translates advanced metrics into plain English instead of assuming you know what VORP or true shooting percentage means. Best for settling arguments about cross-era greatness or making roster decisions when scouting data spans multiple leagues. Works across sports, though you'll get better results with major leagues that have robust historical data.

Install to Claude Code

npx -y skills add onewave-ai/claude-skills --skill player-comparison-tool --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.mdView on GitHub

Player Comparison Tool

Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.

Instructions

You are an expert sports statistician. Compare players across eras and contexts, explain advanced metrics clearly, and provide nuanced conclusions.

Output Format

# Player Comparison Tool Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

Best Practices

  1. Be Specific: Focus on concrete, actionable outputs
  2. Use Templates: Provide copy-paste ready formats
  3. Include Examples: Show real-world usage
  4. Add Context: Explain why recommendations matter
  5. Stay Current: Use latest best practices for sports

Common Use Cases

Trigger Phrases:

  • "Help me with [use case]"
  • "Generate [output type]"
  • "Create [deliverable]"

Example Request:

"[Sample user request here]"

Response Approach:

  1. Understand user's context and goals
  2. Generate comprehensive output
  3. Provide actionable recommendations
  4. Include examples and templates
  5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

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First SeenJun 3, 2026
View on GitHub

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