Opinionated conventions for modeling, indexing and running MongoDB in production.
Sizes database connection pools for Postgres/MySQL apps from a short JSON of services, pods, workers, threads and pool settings - worst-case connections at autoscale max and during deploy surges vs max_connections, busy connections from rps x hold time, per-process wait odds (Erlang C), CPU and memory limits, PgBouncer client and server limits. Recommends pool sizes or proxy settings. Includes an offline stdlib Python calculator with --self-test.
Act as a Supabase Principal Architect. Build and optimize a production-ready Postgres/Edge infrastructure. Your responsibilities include running pg_cron for auditing schemas, addressing RLS alignment gaps, eliminating unused indexes, and auto-generating target indexing definitions. Additionally, construct real-time broadcast tables for tracking states across OpenHands, Obsidian storage pipelines, Hermes, KAI9000, LangGraph, and GitHub workflows. Deploy Edge Functions to manage dynamic webhooks f
Reviews PostgreSQL and MySQL schema migrations (raw SQL or ORM-generated) for table locks, rewrites, data loss, and breaking changes, then proposes safe zero-downtime rewrites with a clear verdict.
Reviews a schema and slow SQL queries, proposes composite indexes in the right column order (equality, sort, range) with covering columns and tradeoffs, flags non-sargable filters like functions on columns and leading-wildcard LIKE, and warns about duplicate, redundant, and low-selectivity indexes. Use when a user shares a slow query, EXPLAIN output, or schema and asks "what index do I need?" or "too many indexes?". Includes a tested stdlib Python helper.
Plans safe API call budgets across services and environments: maps quotas and burst limits, sizes caches and queues, and spots which jobs will trip 429s before they hit production.
Finds preview and ephemeral environments left running after a pull request merges or goes stale, estimates what they cost, and proposes a safe teardown list you approve.
Plans realistic load tests before a launch or traffic spike: models expected traffic, writes test scenarios, reads the results and points to the first bottleneck to fix.
Finds out why your CI builds are slow: tracks job times over weeks, spots missing caches, serial steps and oversized runners, and suggests concrete changes ranked by minutes saved.
