Add module metadata for competition module

Added module.yaml for Module 20 (Competition & Validation):
- Module configuration and learning objectives
- Prerequisites and skill development tracking
- Test coverage and connection documentation

This module brings together all optimization techniques learned
in modules 14-18 for competition preparation.

modules/20_competition/module.yaml

@@ -0,0 +1,59 @@
+name: "Competition & Validation"
+module_number: "20"
+description: "TorchPerf Olympics preparation - validation, baseline, and competition submission"
+difficulty: "⭐⭐⭐" # 3 stars - capstone integration
+estimated_time: "1-2 hours"
+
+prerequisites:
+  - "Module 19: Benchmarking"
+  - "Modules 14-18: Optimization techniques"
+
+learning_objectives:
+  - "Validate TinyTorch installation and environment"
+  - "Generate baseline performance metrics"
+  - "Understand complete optimization workflow"
+  - "Create standardized competition submissions"
+
+key_concepts:
+  - "System validation and environment checks"
+  - "Baseline generation and reference metrics"
+  - "End-to-end optimization workflow"
+  - "Competition submission format"
+
+skills_developed:
+  - "Systematic validation and testing"
+  - "Performance measurement and comparison"
+  - "Integration of multiple optimization techniques"
+  - "Professional submission preparation"
+
+exports_to: "tinytorch/competition/submit.py"
+
+test_coverage:
+  - "Installation validation"
+  - "Baseline generation"
+  - "Worked example workflow"
+  - "Competition template structure"
+
+connections:
+  builds_on:
+    - "Module 19 for benchmarking tools"
+    - "Modules 14-18 for optimization techniques"
+  enables:
+    - "TorchPerf Olympics competition participation"
+    - "Systematic performance optimization"
+    - "Professional ML systems workflow"
+
+notes: |
+  This is the capstone module that brings together all previous modules.
+  It's lightweight (no new techniques) but shows the complete workflow from
+  validation through optimization to submission.
+  
+  Students learn:
+  1. How to validate their environment works
+  2. What baseline performance looks like
+  3. How to apply optimizations systematically
+  4. How to package work for competition
+  
+  The module includes a complete worked example and a template for students
+  to implement their own optimization strategies.
+