What problem does it solve? Before committing to a scaling approach, teams need to know how comparable systems actually behaved under load, but that evidence is scattered across engineering blogs, post-mortems, benchmarks, and papers with no consistent way to collect, date, or compare it. ## Core Features & Use Cases - Four-wave survey pipeline: builds a scale vocabulary map, executes one search angle's coverage grid, extracts one source's episodes, and synthesizes a scale envelope index, each as schema-validated YAML. - Deterministic validation gate: every artifact is checked by scripts/validate_scale_prior_art.py against JSON schemas, with queue-to-record reconciliation and derived load-band and confidence checks. - Evidence discipline: every measurement is recorded with its configuration, date, and load band; undated claims are refused because hardware generations age underneath them. - Use Case: Before writing an ADR for a high-concurrency ingest tier, dispatch the survey to gather dated production episodes and benchmarks, then read the resulting scale envelope index for hard limits, failure modes, and migration triggers. ## Quick Start Use scale-prior-art-survey to survey prior art on tail latency and capacity limits for our ingest tier before we commit to a scaling design.
