ai4pb-task-support

To generate per-task implementation briefs, bridge the gap between architecture knowledge graphs and task execution inputs by reconciling SystemArchitecture.json with task metadata to output actionable guidance for downstream LLM consumption.

Task reconciliation resolves conflicting sources by cross-referencing architectural KG metadata against SCRUM task lists, ensuring traceability and consistency from knowledge graph elements to execution steps.

Yes, you can align SCRUM task execution with system design by querying the architecture knowledge graph to map task IDs and relationships, producing structured implementation briefs for execution readiness.

Parsing SystemArchitecture.json for LLM consumption involves extracting architecture element metadata and task contexts, then formatting them into precise implementation briefs stored within the implementation taskhelpinfos directory.

No specific dependencies are required to bridge architectural KG data and task execution inputs; the process relies on querying existing design knowledge graphs and SystemArchitecture.json files to produce structured task briefs.

Architecture knowledge graph task traceability fails during execution when conflicting sources are not reconciled, preventing accurate mapping of task IDs and relationships to actionable implementation briefs.