ionfury/homelab

architecture-review

Architecture evaluation criteria and technology standards for the homelab. Preloaded into the designer agent to ground design decisions in established patterns and principles. Use when: (1) Evaluating a proposed technology addition, (2) Reviewing architecture decisions, (3) Assessing stack fit for a new component, (4) Comparing implementation approaches. "technology comparison", "design review", "architecture decision"

First seen Feb 25, 2026

Installation

$ npx skills add ionfury/homelab --skill architecture-review

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Gemini CLI Not declared
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Repository health

Stars 25
License LICENSE
Default branch main
Open issues 6
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,253 B
  • docs SUMMARY.md 534 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 45 installs

SKILL.md

Architecture Evaluation Framework

Current Technology Stack

Current technology stack: see [references/technology-decisions.md](references/technology-decisions.md)

Evaluation Criteria

When evaluating any proposed technology addition or architecture change, score against these criteria:

1. Principle Alignment

Score each core principle (Strong/Weak/Neutral):

  • Enterprise at Home: Does it reflect production-grade patterns?
  • Everything as Code: Can it be fully represented in git?
  • Automation is Key: Does it reduce or increase manual toil?
  • Learning First: Does it teach valuable enterprise skills?
  • DRY and Code Reuse: Does it leverage existing patterns or create duplication?

2. Stack Fit

  • Does this overlap with existing tools? (e.g., adding Redis when Dragonfly exists)
  • Does it integrate with the GitOps workflow? (Must be Flux-deployable)
  • Does it work on bare-metal? (No cloud-only services)
  • Does it support the multi-cluster model? (dev → integration → live)

3. Operational Cost

  • How is it monitored? (Must integrate with kube-prometheus-stack)
  • How is it backed up? (Must have a recovery story)
  • How does it handle upgrades? (Must be declarative, ideally via Renovate)
  • What's the failure blast radius? (Isolated > cluster-wide)

4. Complexity Budget

  • Is the complexity justified by the learning value?
  • Could a simpler existing tool solve the same problem?
  • What's the maintenance burden over 12 months?

5. Alternative Analysis

  • What existing stack components could solve this? (Always check first)
  • What are the top 2-3 alternatives in the ecosystem?
  • What do other production homelabs use? (kubesearch research)

6. Failure Modes

  • What happens when this component is unavailable?
  • How does it interact with network policies? (Default deny)
  • What's the recovery procedure? (Must be documented in a runbook)
  • Can it self-heal? (Strong preference for self-healing)

Anti-Patterns to Challenge

Anti-Pattern Why It's Wrong Correct Approach
"Just run a container" without monitoring Invisible failures, no alerting ServiceMonitor + PrometheusRule required
Adding a new tool when existing ones suffice Stack bloat, maintenance burden Evaluate existing stack first
Skipping observability "for now" Technical debt that never gets paid Monitoring is day-1, not day-2
Cloud-only services Vendor lock-in, can't run on bare-metal Self-hosted alternatives preferred
Single-instance without HA story Single point of failure At minimum, document recovery procedure