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
Break the @alkdev/taskgraph architecture specs into dependency-ordered
implementation tasks across 8 component directories: setup, schema,
error, graph, analysis, cost-benefit, frontmatter, api, and review.
Each task has clear acceptance criteria referencing specific architecture
docs. Three review tasks serve as quality gates at critical junction
points (schemas-and-errors, graph-complete, complete-library). The
dependency graph is validated acyclic with 9 topological levels enabling
significant parallelism across independent work streams.
