subagent▌

Spawn autonomous subagents to offload context-heavy work and preserve parent token budget. \n \n Subagents burn their own tokens and return only final results, ideal for deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, and long-running operations \n Choose between mini model (gpt-5.1-codex-mini) for pure search tasks or inherit parent model for multi-step analysis, refactoring, and generation work \n Supports up to 5 parallel subagents via background shell exec

Dispatch fresh subagents per task with two-stage review (spec compliance, then code quality) in the current session. \n \n Isolates each task to a dedicated subagent with precisely crafted context, preventing context pollution and keeping the controller focused on coordination \n Enforces a two-stage review cycle: spec compliance reviewer confirms the implementation matches requirements, then code quality reviewer checks for issues \n Handles implementer status signals (DONE, DONE_WITH_CONCERNS,

This skill provides guidance for creating effective, agent-agnostic subagents.

Execute plan by dispatching fresh subagent per task, with two-stage review after each: spec compliance review first, then code quality review.

Dispatch fresh subagents per task with two-stage review for fast, high-quality implementation execution. \n \n Assigns one subagent per task with spec compliance review first, then code quality review, ensuring both correctness and code standards \n Keeps execution in the current session with automatic review loops that re-run when issues are found, eliminating context switches \n Subagents ask clarifying questions before implementation begins and self-review their work before handoff to reviewe