Backend: Development & Code Tools for Claude Code | AI Skill Market | AI Skill Market

Backend: Development & Code Tools for Claude Code | AI Skill Market | AI Skill Market

AI Skill Market Insights

Real data. Real impact.

Popularity

Rising

Emerging

Active Users

0+

Developers

Time Saved

2+ hrs

Per week

Source

GitHub

Open source

Be Part of the 0+ Developer Community

Skills give you superpowers. Install in 30 seconds.

Error Handling

Never expose stack traces to clients—log internally, return generic message
Structured error responses: code, message, request ID—enables debugging without leaking
Fail fast on bad input—validate at entry point, not deep in business logic
Unexpected errors: 500 + alert—expected errors: appropriate 4xx

Input Validation

Validate everything from outside—query params, headers, body, path params
Whitelist valid input, don't blacklist bad—reject unknown fields
Validate early, before any processing—save resources, clearer errors
Size limits on all inputs—prevent memory exhaustion attacks

Timeouts Everywhere

Database queries: set timeout, typically 5-30s
External HTTP calls: connect timeout + read timeout—don't wait forever
Overall request timeout—gateway or middleware level
Background jobs: max execution time—prevent zombie processes

Retry Patterns

Exponential backoff: 1s, 2s, 4s, 8s...—prevents thundering herd
Add jitter: randomize delay—prevents synchronized retries
Idempotency keys for non-idempotent operations—safe to retry
Circuit breaker for failing dependencies—stop hammering, fail fast

Database Practices

Connection pooling: reuse connections—creating is expensive
Transactions scoped minimal—hold locks briefly
Read replicas for read-heavy workloads—separate read/write traffic
Prepared statements always—SQL injection prevention, query plan cache

Caching Strategy

Cache invalidation strategy decided upfront—TTL, event-based, or both
Cache at right layer: query result, computed value, HTTP response
Cache stampede prevention—lock or probabilistic early expiration
Monitor hit rate—low hit rate = wasted resources

Rate Limiting

Per-user/IP limits on expensive operations—login, signup, search
Different limits for different operations—read vs write
Return Retry-After header—tell clients when to retry
Rate limit early in request pipeline—save resources

Health Checks

Liveness: is process running—restart if fails
Readiness: can handle traffic—remove from load balancer if fails
Startup probe for slow-starting services—don't kill during init
Health checks fast and cheap—don't hit database on every probe

Graceful Shutdown

Stop accepting new requests first—drain load balancer
Wait for in-flight requests to complete—with timeout
Close database connections cleanly—prevent connection leaks
SIGTERM handling: graceful; SIGKILL after timeout

Logging

Structured logs (JSON)—parseable by log aggregators
Request ID in every log—trace request across services
Log level appropriate: debug for dev, info/error for prod
Sensitive data never logged—passwords, tokens, PII

API Design

Versioning strategy from day one—path (/v1/) or header
Pagination for list endpoints—cursor or offset; include total count
Consistent response format—same envelope everywhere
Meaningful status codes—201 for create, 204 for delete, 404 for not found

Security Hygiene

Secrets from environment or vault—never in code or config files
Dependencies updated regularly—automated with Dependabot/Renovate
Principle of least privilege—service accounts with minimal permissions
Authentication and authorization separated—who you are vs what you can do

Observability

Metrics: request count, latency percentiles, error rate—the RED method
Distributed tracing for microservices—follow request across services
Alerting on symptoms, not causes—high error rate, not CPU usage
Dashboards for operational visibility—know normal to spot abnormal