test/cli-docstring-example-validation
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583e6b7ea2
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feat(correcting-plans): implement Predictive Error Prevention (Layer 4) with Error Pattern Database
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Implement spec-mandated Layer 4 Predictive Error Prevention system: - ErrorPattern domain model with pattern text, historical failures, preventive checks, frequency tracking, and keyword-based matching. - ErrorPatternRepository with in-memory CRUD + context-matching query. - ErrorPatternService with record_failure(), match_patterns(), and get_statistics() methods. - Wire into plan execution via plan_lifecycle_service pre-execution hook. - Add error pattern statistics to CLI diagnostics output. Behave BDD: 11 scenarios covering recording, matching, formatting, stats. Robot Framework: 3 integration smoke tests. ASV benchmarks: pattern matching performance. ISSUES CLOSED: #571 |
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f6d27de1cd
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feat(extensibility): implement pluggable scope chain resolution
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Introduce ComponentResolver with a deterministic 3-level scope chain (plan > project > global) for resolving pluggable Protocol-based components. The resolver supports caching, thread-safety, config.toml extension loading, plan metadata loading, introspection APIs, and security-restricted dynamic imports. Includes 39 BDD scenarios, 9 Robot Framework integration tests, ASV benchmarks, and vulture whitelist updates. 100% code coverage on component_resolver.py; overall coverage at 97.07%. ISSUES CLOSED: #552 |