Add comprehensive copilot-instructions.md for repository onboarding

[Copilot]

What does the code in this PR do / what does it improve?

Adds .github/copilot-instructions.md - a comprehensive onboarding document enabling coding agents to work effectively with the fuse codebase on first encounter. Covers project architecture, development workflows, plugin patterns, and common failure modes with workarounds.

Can you briefly describe how it works?

Core sections:

• Architecture: Plugin-based system built on strax framework, base classes (FuseBasePlugin, FuseBaseDownChunkingPlugin), dependency chains
• Development setup: Installation (PyPI, source, NESTpy build workarounds), Python 3.9+ support (CI tests 3.10-3.11)
• Plugin development: Complete template with dtype conventions, URLConfig patterns, deterministic seeding via self.rng, Numba optimization
• Testing: pytest integration tests with MongoDB dependency, timeout patterns, simulation contexts
• Code style: Pre-commit hooks (black, flake8, mypy, docformatter), max line length 100

Common issues documented (7):

1. Missing utilix config (ignorable for basic dev)
2. NESTpy installation (specific commit fb3804e, CMake patch required)
3. MongoDB 4.4.1 for tests (EOL warning included)
4. Strax/straxen version compatibility (min: strax 1.6.0, straxen 2.2.3)
5. Pre-commit hook failures
6. Numba import errors (NumPy 2.0+ compatibility notes)
7. Documentation build

Can you give a minimal working example (or illustrate with a figure)?

Example plugin structure from the guide:

@strax.export
class ElectronDrift(FuseBasePlugin):
    __version__ = "0.1.0"
    depends_on = "clustered_interactions"
    provides = "electron_drift_data"
    
    drift_velocity = straxen.URLConfig(
        default="take://resource://SIMULATION_CONFIG_FILE.json?fmt=json&take=drift_velocity_liquid",
        type=(int, float),
        cache=True,
        help="Drift velocity in liquid xenon [cm/ns]",
    )
    
    dtype = [
        (("Drift time [ns]", "drift_time"), np.int32),
        (("Number of electrons", "n_electron"), np.int32),
    ] + strax.time_fields
    
    def compute(self, clustered_interactions):
        if len(clustered_interactions) == 0:
            return np.zeros(0, dtype=self.dtype)
        # Use self.rng for deterministic random generation
        result = self.process_with_rng(clustered_interactions)
        return result

Please include the following if applicable:

• Update the docstring(s) - N/A, documentation only
• Bump plugin version(s) - N/A, no plugin changes
• Update the documentation - Primary deliverable
• Tests to check the (new) code is working as desired. - Validated markdown structure, content coverage
• Does it solve one of the GitHub open issues?

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