magnus919_agent-skills/backend-engineering

Backend Engineering

Backend engineering methodology — API implementation patterns (REST, gRPC, GraphQL), service architecture (clean/hexagonal/layered), event-driven application flows, outbox/inbox coordination, migration coexistence seams, database access patterns, integration and middleware design, error handling, and service-level testing. Language and framework agnostic.

Why Install This Skill

Your agent gains structured patterns for API design, service architecture, database access, error handling, and integration — instead of improvising each time. Fillable templates turn service designs and error contracts into reviewable records, and the bundled N+1 query spotter catches a whole class of database performance bugs during review.

What You Get

Directory	Purpose
SKILL.md	Core methodology, trigger conditions, reference index
references/	Deep-dive reference files loaded on demand
templates/	Fillable records: service design, event flow/coexistence, and error-handling taxonomy
scripts/	n1-query-spotter.py — scans Python source for potential N+1 query patterns
evals/	Output-quality eval manifest for the skill's methodology cases

Triggers

Building or reviewing APIs, designing service layers, implementing database access patterns, adding error handling, integrating external services, publishing or consuming domain events, implementing outbox/inbox delivery, or keeping old and new service paths safe during a migration.

Do not load this skill as the owner of API/event contracts, service decomposition strategy, schema/pipeline operations, or cross-system migration lifecycle; route those decisions to the linked specialist skills.

Requirements

Platform-agnostic. Applicable to any language/framework stack. The bundled script needs only Python 3 (standard library).

Quick Start

Scan a service for potential N+1 query patterns before a performance review:

python3 backend-engineering/scripts/n1-query-spotter.py services/orders.py

Each finding points at the query call, the enclosing loop, and whether the loop variable is used in the query (high confidence vs possible). Add --json for machine-readable output, and run it from CI — the script exits 1 when findings exist.

Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.