What problem does it solve? Teams set reliability targets from aspiration instead of measured baselines, or run error budgets that change nothing when exhausted. This Skill turns SLOs into targets with policies attached: measurable SLIs per user journey, numbers justified by data, and pre-agreed consequences when the budget burns. ## Core Features & Use Cases - SLI Selection per User Journey: Choose availability, latency, or freshness indicators measured at the user's side of the boundary, with a precise written spec of what counts as a good request. - Baseline-Driven Target Setting: Set SLO numbers from 4+ weeks of measured actuals, bounded by dependency ceilings and the cost of each additional nine. - Error Budget Policy: Derive the budget from the SLO and attach signed policies for healthy, fast-burning, and exhausted states before a crisis forces negotiation. - Use Case: A search service with chronic reliability arguments sets 99.9% availability and 97% latency SLOs on a 30-day rolling window; when a bad deploy burns 60% of budget, the pre-agreed freeze policy turns a release decision into a lookup instead of a fight. ## Quick Start Define SLOs for my search service using the last six weeks of latency and availability data, including an error budget policy.
