Nm Archetypes Architecture Paradigm Space Based

Apply data-grid architecture for high-traffic stateful workloads with in-memory processing and linear scalability

Security Scan

This is an instruction-only architecture guidance skill about space-based data-grid patterns; it contains no code, does not request credentials or install software, and is internally consistent with its stated purpose.

Purpose & Capability

Name and description match the SKILL.md content: a conceptual guide for space‑based/data‑grid architectures. The skill does not request unrelated resources or credentials.

Instruction Scope

SKILL.md provides high‑level adoption steps, deliverables, risks, and troubleshooting. It does not instruct the agent to read files, access environment variables, contact external endpoints, or run arbitrary commands beyond generic troubleshooting hints.

Install Mechanism

No install spec and no code files are present (instruction-only), so nothing is written to disk or fetched during install. This is the lowest-risk install model.

Credentials

The skill declares no required environment variables, credentials, or config paths. There are no requests for secrets or unrelated service keys.

Persistence & Privilege

always is false, user-invocable is true (normal), and the skill does not request persistent system changes or access to other skills' configuration.

Scan Findings in Context

[no_findings] expected: Regex scanner found nothing to analyze; that is expected because this is an instruction-only SKILL.md with no code files.

Assessment

This skill is a harmless architecture guide. Before relying on it in production, review any concrete implementation or tooling you adopt based on these recommendations. If you later install plugins or code (the SKILL references a 'Claude Code' plugin), inspect their code and install sources, because instruction-only skills are low-risk by themselves but connected plugins can introduce code or network behavior.

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

The Space-Based Architecture Paradigm

When To Use

• High-traffic applications needing elastic scalability
• Systems requiring in-memory data grids

When NOT To Use

• Low-traffic applications where distributed caching is overkill
• Systems with strong consistency requirements over availability

When to Employ This Paradigm

• When traffic or state volume overwhelms a single database node.
• When latency requirements demand in-memory data grids located close to processing units.
• When linear scalability is required, achieved by partitioning workloads across many identical, self-sufficient units.

Adoption Steps

1. Partition Workloads: Divide traffic and data into processing units, each backed by a replicated data cache.
2. Design the Data Grid: Select the appropriate caching technology, replication strategy (synchronous vs. asynchronous), and data eviction policies.
3. Coordinate Persistence: Implement a write-through or write-behind strategy to a durable data store, including reconciliation processes.
4. Implement Failover Handling: Design a mechanism for leader election or heartbeats to validate recovery from node loss without data loss.
5. Validate Scalability: Conduct load and chaos testing to confirm the system's elasticity and self-healing capabilities.

Key Deliverables

• An Architecture Decision Record (ADR) detailing the chosen grid technology, partitioning scheme, and durability strategy.
• Runbooks for scaling processing units and for recovering from "split-brain" scenarios.
• A monitoring suite to track cache hit rates, replication lag, and failover events.

Risks & Mitigations

• Eventual Consistency Issues:
  • Mitigation: Formally document data-freshness Service Level Agreements (SLAs) and implement compensation logic for data that is not immediately consistent.
• Operational Complexity:
  • Mitigation: The orchestration of a data grid requires mature automation. Invest in production-grade tooling and automation early in the process.
• Cost:
  • Mitigation: In-memory grids can be resource-intensive. Implement aggressive monitoring of utilization and auto-scaling policies to manage costs effectively.

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