FastAPI production best practices — domain-organized project structure, async correctness, DI patterns with per-request caching and chaining, Pydantic v2 split create/update/response models, Pydantic Settings v2, async testing with httpx.AsyncClient.
FastAPI production-grade best practices — covers project structure (domain-organized modules), async correctness (no blocking I/O in async paths, no CPU work in BackgroundTasks), dependency injection patterns with per-request caching and chaining, Pydantic v2 model design (split create/update/response, ConfigDict…
Git workflow best practices — Git Flow branch prefixes, Conventional Commits format, atomic commits with imperative mood, pre-commit hygiene, pull --rebase before push, no force-push to shared branches, max 400 LOC per PR, squash on merge for clean main history.
Engineering rules for building an interactive (online) gaming platform that can pass a GLI-19 technical certification — server-authoritative game outcomes with no game logic on the client, one authoritative system clock, the records a gaming system must maintain (per-game play record, per-theme aggregates with…
Engineering rules for building an interactive (online) gaming platform that can pass a GLI-19 technical certification — server-authoritative game outcomes, authoritative system clock and time-sync, the records a gaming system must be able to produce (per-game play record, per-theme aggregates, player-account record…
Security-engineering rules for an interactive gaming platform under the GLI Gaming Security Framework (GLI-GSF-1) — the deep security-controls layer beneath GLI-19's technical-security appendix, owning the infrastructure and network security territory (DNS, firewalls, remote access, hardening, segmentation, IDS/IPS…
Security-engineering rules for an interactive gaming platform under the GLI Gaming Security Framework (GLI-GSF-1) — the deep security-controls layer that GLI-19's technical-security appendix only summarizes. Covers identity and logical access control (individual credentials, RBAC with least privilege, separation of…
Python gRPC async-first Kubernetes-native best practices using grpc.aio — server bootstrap with graceful shutdown, shared per-process channel factory, canonical decorator pattern for cross-cutting concerns, maxconnectionage for HPA rebalancing, client-side LB via dns:/// + roundrobin over a headless Service (no se.
Python gRPC best practices for async-first services using grpc.aio in Kubernetes — covers project structure (.proto files, committed generated stubs), protobuf message design and field-number stability, async server bootstrap with graceful shutdown that flips Health to NOTSERVING before server.stop(), async client…
Engineering best practices for money / payments / ledger systems — Decimal or integer minor units (never float), the two-layer idempotency model (client-driven idempotencykey + chain-CAS for state transitions, never conflated), double-entry ledger architecture (Accounts + Transfers, debit=credit invariant at DB level.
Engineering best practices for systems that handle money, payments, and ledgers — money representation (Decimal or integer minor units, never float; currency code as separate field; explicit rounding modes), the two-layer idempotency model (client-driven idempotencykey for request dedup AND chain-CAS for state…
Python observability best practices using OpenTelemetry — SDK bootstrap with off-switch contract and idempotency guard, auto-instrumentation (gRPC / SQLAlchemy / FastAPI / Logging), the LoggingInstrumentor + structlog correlation pattern with custom log hook and CleanLoggingHandler, two-tier log field taxonomy, span…
Python observability best practices for async-first services using OpenTelemetry — covers SDK bootstrap with setuptelemetry(component) and an OTELEXPORTEROTLPENDPOINT off-switch contract, single-shot idempotency guard, per-component service.name resource attribute, OTLP HTTP/protobuf exporters with BatchSpanProcessor…
pytest async-first testing best practices — test layer structure, fixture design, async testing with pytest-asyncio, test data with factoryboy + faker, HTTP mocking with respx, time mocking with freezegun, property-based testing with hypothesis, DB transaction-rollback isolation, flush() pattern for chained fixtures.
This skill captures production-tested defaults for pytest with async support, factory-driven test data, real-DB integration testing, and CI-friendly configuration. It assumes the test target is a Python backend (FastAPI, SQLAlchemy, async services). UI / Selenium / Playwright e2e patterns are out of scope.
SQLAlchemy 2.0 async-first best practices — covers engine and pool config, model design with Mapped[T] type annotations, relationship loading strategies (selectin / select / writeonly), query optimization, async session and transaction patterns, and FastAPI dependency injection. Use this skill whenever the user is…