Nw Production Readiness Agent Skill
Monitoring, observability, operational procedures, CI/CD lessons learned, and quality gate definitions. Load when assessing production readiness or validating operational excellence.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/nWave-ai/nWave --skill nw-production-readinessThe instruction itself
10 sections, as written by the authorProduction Readiness
Monitoring and Observability
Application Monitoring
- Performance: response time | throughput | latency percentiles (P50, P95, P99)
- Resources: CPU | memory | database connections | cache hit rates
- Errors: exception tracking | error rate trends | integration failure detection
- Business: KPI tracking | conversion funnels | feature usage | revenue impact
Infrastructure Monitoring
Server/container health and resource utilization | Network performance and connectivity | Storage capacity and I/O performance | Security event detection.
Alerting Tiers
| Tier | Condition | Response |
|------|-----------|----------|
| Page | Service down, data loss risk, security breach | Immediate response |
| Urgent | Error rate >2x baseline, latency SLA breach | Response within 15 min |
| Warning | Capacity >80%, error rate trending up | Response within 1 hour |
| Info | Deployment complete, metric threshold crossed | Review next business day |
Operational Procedures
Incident Response
- Detect: automated alerting identifies issue
- Triage: classify severity, assign responder
- Communicate: notify stakeholders per severity level
- Resolve: apply fix or rollback
- Review: post-incident review within 48 hours
- Improve: update runbooks and monitoring based on findings
Maintenance Procedures
Regular update and patching schedule | Backup verification (test restores quarterly) | Security vulnerability scanning (automated, weekly) | Performance baseline recalibration (after major changes).
Knowledge Transfer
Operational runbooks for common procedures | Architecture documentation with system diagrams | Deployment procedures and configuration management | Troubleshooting guides for known failure modes.
Quality Gates for Production Readiness
Before declaring production-ready, all must pass:
- [ ] All acceptance tests passing
- [ ] Unit coverage meets project standard (default: >= 80%)
- [ ] Integration tests validated
- [ ] Performance validated under realistic load
- [ ] Security scan completed (0 critical, 0 high)
- [ ] Monitoring and alerting configured
- [ ] Logging structured and searchable
- [ ] Rollback procedure documented and tested
- [ ] Runbook created for operational procedures
- [ ] On-call team trained on new feature
For CI/CD architecture lessons and measurement coupling pitfalls, see cicd-and-deployment skill.
Other skills for the same job
different authors, same section of the catalogue
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
How to use it
Take nwave-ai/nw-production-readiness from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.