What problem does it solve? Running autonomous AI agents in continuous loops often leads to churn without progress, repeated failures on the same root cause, stalled merge queues, and uncontrolled cost escalation. This Skill provides structured patterns for selecting, combining, and recovering continuous agent loops so they stay measurable and controllable. ## Core Features & Use Cases - Loop Selection Flow: Routes work to the right loop type—continuous-pr for strict CI/PR control, rfc-dag for RFC decomposition, infinite for exploratory parallel generation, or sequential as the default. - Combined Production Pattern: Recommends a stack of RFC decomposition, quality gates, an eval loop, and session persistence for production-grade autonomy. - Failure Recovery: Defines concrete recovery steps—freeze the loop, run a harness audit, reduce scope to the failing unit, and replay with explicit acceptance criteria. - Use Case: A team running an overnight autonomous coding agent notices it retrying the same failing test for hours. They apply the recovery pattern: freeze the loop, audit the harness, narrow scope to the failing unit, and restart with explicit acceptance criteria. ## Quick Start Ask the agent to set up a continuous agent loop with quality gates and evals for your current coding task, choosing the appropriate loop type from the selection flow.
