glm-5.1
7bfcfecc7a
Implement computeEffectiveP internal helper and workflowCost public function that captures the structural reality that upstream failures multiply downstream damage, per ADR-004 and the Python research model. - computeEffectiveP: computes pEffective from intrinsic probability + upstream propagation using inherited quality factors (parentP + (1-parentP) × qualityRetention) - workflowCost: processes tasks in topological order, computes per-task EV with degraded effective probability, includes pIntrinsic/pEffective split - Supports independent and dag-propagate modes - Completed tasks excluded from results but propagate p=1.0 when includeCompleted: false - Per-edge qualityRetention overrides defaultQualityRetention option - Throws CircularDependencyError for cyclic graphs via topologicalOrder - 30+ new tests covering chain compounding, diamond graph, mode comparison, completed task semantics, cycle detection, per-edge qualityRetention