Jason Woltje
5639d085b4
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>
6 lines
89 B
Plaintext
6 lines
89 B
Plaintext
# Python dependencies for coordinator scripts
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# Testing
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pytest>=9.0.0
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pytest-cov>=7.0.0
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