production-readiness-skill

name	production-readiness-skill
description	Prepare backend services for deployment with explicit configuration, health, observability, capacity, performance, shutdown, and failure-isolation behavior.
allowed-tools	["read_file","list_dir","grep_search","glob_files","write_file","edit_file","apply_patch","lsp","run_command","artifact_write"]

Backend production readiness

Overview

Production readiness is the service's ability to start, serve, degrade, stop, and recover predictably under real load and dependency failure. Measure the critical path before adding complexity.

When to Use

Use for containers, deployment manifests, runtime configuration, health checks, logging, metrics, tracing, alerts, performance, caching, connection pools, capacity, and incident hardening.

Workflow

1. Identify the service boundary, dependencies, startup contract, critical user path, resource limits, expected load, and failure budget.
2. Separate non-secret configuration from write-only secrets. Validate required configuration at startup without echoing sensitive values.
3. Implement distinct liveness and readiness semantics. Readiness must reflect whether the instance can serve safely without turning transient dependency failures into restart loops.
4. Add structured, bounded observability for requests and jobs: stable operation names, correlation, latency, outcomes, saturation, retries, and error classes. Avoid sensitive or unbounded fields.
5. Measure before tuning. Address algorithm, query, payload, batching, connection, or cache behavior at the bottleneck and keep invalidation and consistency explicit.
6. Verify graceful shutdown, draining, dependency timeout, overload, degraded mode, rollback, and recovery using the smallest production-representative environment available.

Verification

• Startup validates configuration without exposing secrets.
• Liveness, readiness, shutdown, and draining semantics are correct.
• Logs, metrics, and traces explain latency, errors, retries, and saturation.
• Performance work is based on measurement and preserves correctness.
• Dependency failure, overload, rollback, and recovery were exercised.

Red Flags

• Health endpoints that always return success.
• Unbounded request labels, payloads, logs, queues, or in-memory caches.
• Increasing timeouts or replicas without locating the bottleneck.
• Container success treated as proof of application readiness.