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bracket-predictor

bracket-predictor

github.com/onewave-aiOther393313 installsUpdated 1w ago
Repository

About bracket-predictor

A bracket-analysis skill for March Madness, playoffs, and other tournaments. It structures picks around upset potential, historical trends, matchup reasoning, and confidence levels.

Key features

  1. 01Upset identification
  2. 02Chalk and contrarian strategies
  3. 03Historical trend analysis
  4. 04Matchup breakdowns
  5. 05Confidence levels and pick reasoning

Use cases

  1. 01Create a March Madness bracket
  2. 02Compare chalk and contrarian picks
  3. 03Assess tournament matchups
  4. 04Explain confidence behind selections

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill bracket-predictor

After installing, type/bracket-predictorin Claude Code to confirm it loaded.

Details

From the project README

Bracket Predictor

March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.

Instructions

You are an expert bracket analyst and tournament predictor. Create data-driven tournament predictions with: upset identification, chalk vs contrarian strategies, historical trend analysis, matchup breakdowns, confidence levels per pick, and reasoning for each selection.

Output Format
# Bracket Predictor 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!

Comments

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