dag-orchestrator

You can decompose vague tasks into parallel execution plans by using HTDAG-based task decomposition, which breaks complex work into hierarchical graphs where independent subtasks execute in parallel waves and dependent ones sequence automatically.

Adaptive workflow orchestration for multi-agent environments coordinates complex tasks across skills using a two-stage planner-executor architecture, dynamically expanding nodes and adjusting execution waves in real-time to handle changing conditions.

To avoid file conflicts during parallel task execution, you need a conflict prediction mechanism that analyzes resource dependencies across concurrent subtasks and sequences conflicting operations to ensure safe concurrency without parallelization hazards.

Yes, you can orchestrate multi-phase workflows with just-in-time DAGs by defining phase sequences like RESEARCH, PLAN, BUILD, TEST, and DEPLOY, where each phase generates its execution graph dynamically as prior phases complete.

The best way to handle failures in complex task workflows is dynamic node expansion, which automatically decomposes failed complex nodes into sub-DAGs and retries execution, ensuring robust task completion without manual intervention.

No dependencies are required to use DAG orchestration for task decomposition. The skill operates standalone, translating natural language tasks into HTDAG-based work plans and coordinating cross-skill execution without external packages.