What problem does it solve? Manual sleep loops and ad-hoc retry logic in Effect v4 code are error-prone: they confuse retry with repeat semantics, retry non-idempotent operations, ignore rate-limit headers, and break on removed APIs like Schedule.tapInput. This Skill provides verified guidance for building correct time and recurrence policies with Schedule. ## Core Features & Use Cases - Policy Selection: Choose the right Schedule combinator for counters, fixed delays, cadence alignment, exponential or fibonacci backoff, jitter, and sequential composition via Schedule.concat. - Failure Policy Design: Distinguish typed failures, defects, and interruption; decide per-item versus per-pass failure handling for polling workers and batch processing. - Rate-Limit-Aware Retries: Use Schedule.modifyDelay to honor provider retry-after hints, and apply timeouts with Effect.timeout, timeoutOption, or timeoutOrElse. - Use Case: When wrapping a flaky third-party API call, build a jittered exponential backoff schedule bounded to five attempts that respects the provider's Retry-After header and falls back truthfully on exhaustion. ## Quick Start Ask the assistant to design an Effect v4 retry policy with exponential backoff, jitter, and a bounded attempt count for a specific failing operation.