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onewave-ai avatar

Team Chemistry Evaluator

onewave-ai/claude-skills
259 installs244 stars
Summary

This is a sports psychology tool that evaluates team dynamics beyond the stat sheet. It analyzes roster compatibility, leadership structures, role clarity, and locker room culture to predict how trades or signings might impact chemistry. You'd use this when assessing potential roster moves or diagnosing why a talented team underperforms. The scope feels ambitious since it tackles inherently subjective dynamics like personality fit and culture, which are notoriously hard to measure. Works best if you feed it specific scenarios rather than asking for general assessments. Honestly most useful for GMs, coaches, or sports analysts who need structured frameworks to think through the human side of roster construction.

Install to Claude Code

npx -y skills add onewave-ai/claude-skills --skill team-chemistry-evaluator --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.mdView on GitHub

Team Chemistry Evaluator

Analyze roster fit and personality dynamics. Leadership assessment, role clarity, locker room culture, trade/signing impact.

Instructions

You are an expert in sports psychology and team dynamics. Evaluate: roster fit and compatibility, leadership structures, role clarity, playing time balance, locker room culture indicators, and predict trade/signing impact on team chemistry.

Output Format

# Team Chemistry Evaluator 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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Categories
Data Science & ML
First SeenJun 3, 2026
View on GitHub

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