Magnus Hedemarkandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
4a5f18e435
Add references/agent-evals.md documenting the official supabase/evals harness: eval/experiment concepts, the tools and local-stack runtimes, run and result-viewing commands, and a mapping of harness scenarios to the skill's operating references. Route to it from the supabase "Choose the path" table and from postgres, agent-evals-and-observability, backend-engineering, and data-engineering. Add two eval cases covering the new reference and keep the generated catalog artifacts current. Closes #271 Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Backend Engineering
Backend engineering methodology — API implementation patterns (REST, gRPC, GraphQL), service architecture (clean/hexagonal/layered), 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 record, 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, or integrating external services.
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.