parallel-execution-optimizer

Parallel execution identifies independent operations in your workflow and runs them concurrently in a single batch, delivering 3-5x speedups. Group tasks with no dependencies, then invoke them together to execute multiple operations simultaneously instead of sequentially.

Parallelize when you have independent checks, validations, or operations that don't depend on each other's results—common in optimization phases, pre-flight checks, implementation batching, and design prototyping. Dependency analysis automatically detects these opportunities.

Dependency analysis constructs a graph of task relationships to isolate operations with no shared dependencies. Tasks in the same layer execute concurrently; results aggregate and feed into dependent downstream tasks, maintaining correctness while maximizing throughput.

Built-in failure handling and retry logic catch errors during parallel batches, allowing you to recover or halt gracefully. Results aggregate across all concurrent tasks, ensuring quality is maintained even when individual operations fail.

Yes. Single-message multi-tool invocations execute 3-5 independent tasks concurrently in one call. Group operations with no shared dependencies, then submit the batch as a single message to achieve true parallelism.