Nm Archetypes Architecture Paradigm Microservices

Apply microservices for independent deployment and per-service scaling

Security Scan

This is an instruction-only microservices architecture guidance skill whose requirements and instructions are consistent with its stated purpose and it does not request credentials, installs, or elevated persistence.

Purpose & Capability

The skill name and description (microservices architecture guidance) match the SKILL.md content: design guidance, adoption steps, technology recommendations, risks and mitigations. It does not request unrelated binaries, env vars, or config paths. Minor metadata inconsistency: SKILL.md lists version 1.8.2 while registry metadata lists 1.8.3 (likely a packaging/versioning mismatch).

Instruction Scope

SKILL.md contains only explanatory text and step-by-step architectural guidance. It does not instruct the agent to read system files, environment variables, or transmit data to external endpoints. A note: it references installing the 'Claude Code' plugin for extra features — that plugin should be reviewed separately before installing.

Install Mechanism

There is no install spec and no code files. This is the lowest-risk format (instruction-only); nothing will be written to disk or fetched by the skill itself.

Credentials

The skill declares no required environment variables, credentials, or config paths, which is proportionate for an architecture guidance document.

Persistence & Privilege

The skill does not request always:true or other elevated persistence. It is user-invocable and allows normal autonomous invocation (platform default), which is appropriate for a guidance plugin.

Assessment

This skill is pure documentation and appears safe to add: it asks for no credentials and performs no installs. Before relying on specific tooling recommendations, verify they fit your organization's constraints (cloud provider, regulatory requirements, team maturity). Also note the SKILL.md suggests installing a separate 'Claude Code' plugin for additional functionality — review that plugin's install steps and permissions before installing it. Finally, the version string in the file header (1.8.2) doesn't match the registry version (1.8.3); this is likely benign but you may want to confirm you have the intended release.

Night Market Skill — ported from claude-night-market/archetypes. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

• When to Employ This Paradigm
• When NOT to Use This Paradigm
• Adoption Steps
• Key Deliverables
• Technology Guidance
• Risks & Mitigations
• Troubleshooting

The Microservices Architecture Paradigm

When to Employ This Paradigm

• When the organizational structure requires high levels of team autonomy and independent release cycles.
• When different business capabilities (bounded contexts) have distinct scaling requirements or would benefit from different technology stacks.
• When there is a significant organizational commitment to investing in DevOps and SRE maturity, including advanced observability, CI/CD, and incident response capabilities.

When NOT To Use This Paradigm

• When team size is small and organizational complexity is low
• When lack of DevOps maturity or limited platform engineering resources
• When system requires strong transactional consistency across operations
• When early-stage startup with rapidly evolving requirements
• When regulatory constraints make distributed data management challenging

Adoption Steps

1. Define Bounded Contexts: Map each microservice to a clear business capability and establish unambiguous data ownership.
2. validate Service Data Autonomy: Each service must own and control its own database or persistence mechanism. All data sharing between services must occur via APIs or events, not shared tables.
3. Build a production-grade Platform: Before deploying services, establish foundational infrastructure for service discovery, distributed tracing, centralized logging, CI/CD templates, and automated contract testing.
4. Design for Resilience: Implement resilience patterns such as timeouts, retries, circuit breakers, and bulkheads for all inter-service communication. Formally document Service Level Indicators (SLIs) and Objectives (SLOs).
5. Automate Governance: Implement automated processes to enforce security scanning, dependency management policies, and consistent versioning strategies across all services.

Key Deliverables

• An Architecture Decision Record (ADR) cataloging all service boundaries, their corresponding data stores, and their communication patterns (e.g., synchronous API vs. asynchronous events).
• A set of "golden path" templates and runbooks for creating and operating new services on the platform.
• A detailed testing strategy that includes unit, contract, integration, and chaos/resilience tests.

Technology Guidance

API Communication:

• REST APIs: Spring Boot (Java), Express.js (Node.js), FastAPI (Python)
• GraphQL: Apollo Server (Node.js), Hasura (PostgreSQL)
• gRPC: gRPC frameworks for high-performance internal communication

Service Discovery & Configuration:

• Service Registry: Consul, Eureka, etcd
• Configuration: Spring Cloud Config, HashiCorp Vault, AWS Parameter Store

Message Broking & Events:

• Message Brokers: Apache Kafka, RabbitMQ, AWS SQS/SNS
• Event Streaming: Apache Kafka, Apache Pulsar, AWS Kinesis

Observability:

• Distributed Tracing: Jaeger, Zipkin, AWS X-Ray
• Metrics: Prometheus, Datadog, CloudWatch
• Logging: ELK Stack, Fluentd, Splunk

Real-World Examples

Netflix: Video streaming platform with hundreds of microservices handling different aspects like playback, recommendation, billing, and user authentication. Each team can deploy independently without affecting others.

Amazon: E-commerce platform with separate services for product catalog, order processing, payment, inventory, and shipping. Enables independent scaling during high-traffic events like Prime Day.

Uber: Ride-sharing platform with microservices for rider matching, driver dispatch, pricing, payment processing, and notifications, allowing rapid feature development and deployment.

Risks & Mitigations

• Distributed System Complexity:
  • Mitigation: The operational overhead for a microservices architecture is substantial. Invest in dedicated platform teams and shared tooling to manage this complexity and provide support for service teams.
• Data Consistency Challenges:
  • Mitigation: Maintaining data consistency across services is a primary challenge. Employ patterns like Sagas for orchestrating transactions, validate message-based communication is idempotent, and use reconciliation jobs to handle eventual consistency.
• Incorrect Service Granularity ("Over-splitting"):
  • Mitigation: If services are too small, the communication overhead can outweigh the benefits of distribution. validate each service owns a meaningful and substantial piece of functionality. Monitor change coupling between services to identify candidates for merging.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag