Thicken the two flagship engineering methodology skills with the artifact set promised by issue #239: schema-v1 eval manifests (6 cases each), fillable templates, and one small stdlib-only script per skill with tests. backend-engineering: - evals/evals.json: API implementation review, endpoint modeling, service structure, error handling, N+1 detection, integration retry/idempotency - templates/service-design-record.md, templates/error-handling-taxonomy.md - scripts/n1-query-spotter.py (+ test_n1_query_spotter.py): flags query-like calls inside loops with loop-variable confidence, --json output frontend-engineering: - evals/evals.json: component/state design, state management selection, API integration, data-fetching states, performance review, performance budgets - templates/component-state-design-record.md, templates/performance-budget.md - scripts/bundle-budget-checker.py (+ test_bundle_budget_checker.py): enforces total and per-chunk byte budgets on bundle reports, exit 1 on violation Both SKILL.md files gain Templates and Scripts sections; both READMEs document the scripts in Quick Start. All local validators pass (validate-skills.rb, validate-evals.py, eval-coverage ratchet, make validate). 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.