task-dispatcher

Automating task routing to Codex requires splitting large development requests into atomic subtasks, analyzing dependencies, and dispatching concurrent batches. This skill handles that orchestration by pre-evaluating time limits, validating boundaries, and tracking execution to recombine results successfully.

Dependency-aware batching analyzes subtask relationships to schedule groups for concurrent execution while strictly respecting sequential order. This maximizes parallelism during code generation by ensuring independent tasks run simultaneously and dependent tasks wait for prerequisite completion.

Splitting monolithic coding tasks involves breaking the work into atomic units with clear boundaries, such as separating write, test, and validate phases. This skill automates that decomposition, estimates time per subtask, and requires structured verification steps before recombining the final output.

Timeout handling monitors dispatched subtasks for execution overruns and generates diagnostic guidance when limits are exceeded. This prevents stalled processes from blocking the workflow by enforcing pre-evaluated time limits and providing structured feedback for troubleshooting failures.

Yes, task splitting is designed specifically for AI-assisted software development workflows. It decomposes feature additions into discrete write, test, and validate subtasks, dispatches them to Codex, and recombines the verified results to automate complex coding pipelines.

Automated task dispatch relies on accurate dependency analysis and time estimation to function correctly. Complex tasks with hidden dependencies may exceed pre-evaluated time limits, triggering timeout handling and diagnostic guidance rather than successful autonomous completion.