feat(#154): Implement context estimator

Implements formula-based context estimation for predicting token
usage before issue assignment.

Formula:
  base = (files × 7000) + complexity + tests + docs
  total = base × 1.3  (30% safety buffer)

Features:
- EstimationInput/Result data models with validation
- ComplexityLevel, TestLevel, DocLevel enums
- Agent recommendation (haiku/sonnet/opus) based on tokens
- Validation against actual usage with tolerance checking
- Convenience function for quick estimations
- JSON serialization support

Implementation:
- issue_estimator.py: Core estimator with formula
- models.py: Data models and enums (100% coverage)
- test_issue_estimator.py: 35 tests, 100% coverage
- ESTIMATOR.md: Complete API documentation
- requirements.txt: Python dependencies
- .coveragerc: Coverage configuration

Test Results:
- 35 tests passing
- 100% code coverage (excluding __main__)
- Validates against historical issues
- All edge cases covered

Acceptance Criteria Met:
✅ Context estimation formula implemented
✅ Validation suite tests against historical issues
✅ Formula includes all components (files, complexity, tests, docs, buffer)
✅ Unit tests for estimator (100% coverage, exceeds 85% requirement)
✅ All components tested (low/medium/high levels)
✅ Agent recommendation logic validated

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

scripts/coordinator/.coveragerc

@@ -0,0 +1,14 @@
+[run]
+omit =
+    venv/*
+    test_*.py
+
+[report]
+exclude_lines =
+    pragma: no cover
+    def __repr__
+    raise AssertionError
+    raise NotImplementedError
+    if __name__ == .__main__.:
+    if TYPE_CHECKING:
+    @abstractmethod