What problem does it solve? Parallelizing work across multiple AI agents often backfires: slices share files, contracts stay open, and the merge becomes a costly negotiation that erases any time saved. This Skill provides the decision criteria and execution protocol to know when a parallel graph actually pays off and how to run it without rework. ## Core Features & Use Cases - Go/no-go decision framework: Enforces a closed contract (schema, types, error format) before any fan-out, and requires vertical slices with zero shared files. - Isolation protocol: Assigns each slice its own git worktree with a declared arquivos_exclusivos manifest verified against all other slices in the wave. - Single-owner fan-in: Defines an integrator role that only merges and checks integration seams, never implements, and rejects failed slices back to their worktrees. - Cost awareness: Explains the clock-vs-token tradeoff (N× prefill against subscription quota) and lists cases where parallelizing is pure overhead. - Use Case: When planning a wave with multiple tasks — e.g., building auth and export features simultaneously — use this Skill to verify the slices are truly independent, spin up worktrees, and run a clean single-owner integration. ## Quick Start Ask the agent to evaluate whether your planned multi-task wave qualifies for parallel execution and to set up the worktree-based fan-out and fan-in plan.