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ariaxhanariaxhan/kernel-claude/skills/architecture/SKILL.md

architecture

Skill15 starsChanged 4 months agoMITView on GitHub

Modular design, interface stability, dependency management, AI-code health. Triggers: architecture, design, structure, modules, dependencies, coupling, system design.

136 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 118 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check for established patterns.

    line 22
  2. Distinctive:

    FOLLOW EXISTING PATTERNS: Don't introduce new patterns without justification.

    line 30
  3. Distinctive:

    Before adding AI to a codebase:

    line 40
  4. Distinctive:

    If you can't explain it simply, the design is too complex

    line 48

“Common” is measured: kept by at least 6 in 10 of the 118 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<skill id="architecture"><purpose> Architecture is about managing change over time. Good architecture makes easy things easy and hard things possible. AI-generated code amplifies existing patterns—good or bad. </purpose><prerequisite> Understand existing codebase structure before proposing changes. Check for established patterns. Follow them unless explicitly changing. </prerequisite><reference> Skill-specific: skills/architecture/reference/architecture-research.md </reference>

<core_principles>

  1. FOLLOW EXISTING PATTERNS: Don't introduce new patterns without justification.
  2. INTERFACE STABILITY: Changing interfaces breaks everything. Stabilize them first.
  3. MODULAR BOUNDARIES: Clear separation. Each module has one reason to change.
  4. DEPENDENCY DIRECTION: Depend on abstractions, not concretions. Core doesn't know about edges.
  5. SMALLEST CHANGE: Prefer minimal changes that achieve the goal. </core_principles>

<ai_code_health_nexus> From research: 30%+ defect risk when AI applied to unhealthy code. AI amplifies existing patterns. If code is messy, AI makes it messier. Before adding AI to a codebase:

  1. Identify health score (coupling, complexity, test coverage)
  2. Fix critical health issues first
  3. Establish clear interfaces
  4. Then apply AI within those boundaries </ai_code_health_nexus>

<design_heuristics>

  • If you can't explain it simply, the design is too complex
  • Three concrete examples before abstraction
  • Composition over inheritance
  • Make illegal states unrepresentable
  • Parse, don't validate </design_heuristics>

<anti_patterns>

  • Premature abstraction (abstract before 3 concrete uses)
  • Leaky abstractions (implementation details bleeding through)
  • Circular dependencies
  • God objects (one class doing everything)
  • Feature envy (methods that use another class more than their own) </anti_patterns>

<on_complete> agentdb write-end '{"skill":"architecture","decision":"X","coupling_reduced":true,"adr":"path|none"}' </on_complete>

</skill>

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