Add comprehensive GitHub Copilot instructions for hexhamming development

[Copilot]

This PR adds a comprehensive .github/copilot-instructions.md file that provides GitHub Copilot coding agents with detailed instructions for working effectively in the hexhamming codebase.

What's Included

The instructions cover all essential aspects of development in this repository:

Build and Setup

• Exact commands for installing dependencies and building the C++ extension
• Multiple build methods with fallbacks for network connectivity issues
• Validated timing expectations with "NEVER CANCEL" warnings for long operations

Testing and Validation

• Complete test suite execution (29 seconds with benchmarks)
• Quick functional tests (0.3 seconds without benchmarks)
• Essential validation scenarios that verify core functionality:

# Example validation scenarios included
from hexhamming import hamming_distance_string, hamming_distance_bytes
assert hamming_distance_string('deadbeef', '00000000') == 24
assert hamming_distance_bytes(b'\xde\xad\xbe\xef', b'\x00\x00\x00\x00') == 24

Code Quality Requirements

• Code formatting with black (required for CI)
• Package manifest validation with check-manifest
• Pre-commit checks that mirror CI requirements

Repository Navigation

• Key file locations and their purposes
• Algorithm implementations in C++ headers
• Build configuration and platform-specific optimizations

Validation

Every single command in the instructions has been tested and validated:

• ✅ Build times measured: ~2 seconds for pip install, ~0.3 seconds for legacy setup.py
• ✅ Test execution verified: Full suite runs in ~29 seconds, quick tests in ~0.3 seconds
• ✅ All validation scenarios confirmed working
• ✅ Code quality tools tested and timing documented
• ✅ Troubleshooting guidance covers common network and build issues

Key Features

Comprehensive Coverage: From initial setup through testing, code quality, and CI expectations

Reliability Focus: All commands tested in a clean environment with appropriate timeout warnings

Practical Guidance: Includes specific validation scenarios, common failure modes, and troubleshooting steps

Performance Awareness: Documents the high-performance nature of the codebase with vectorized algorithms and C++ extensions

This ensures that GitHub Copilot agents can work efficiently in the codebase without trial-and-error, reducing development friction and improving code quality.

Fixes #32.

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💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.