noticed-labs

Improve agent eval scores using a structured benchmark loop. Use when eval scenarios fail, judge scores are low, onboarding quality regresses, or when iterating on system prompts, persona overlays, extraction prompts, or onboarding instructions. Covers both onboarding evals (scripted messages, 6 dimensions) and agent live evals (user-sim LLM, mission-driven criteria).

Optimize database queries across this codebase using a local-first benchmark loop. Use when a query is slow, memory-heavy, timing out, causing socket hang ups, scanning too many rows, or when improving ClickHouse/Postgres throughput, joins, aggregates, lookup tables, pipeline concurrency, or benchmark scripts.