Improve Codebase Architecture

SkillDev tools

Analyze a codebase for high-leverage architectural improvements and explain evidence, tradeoffs, and next steps. Use explicitly for architecture audits or deep-module refactoring opportunities.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Improve Codebase Architecture skill

What this skill tells your AI

The instructions your AI receives, as published by stevesolun/ctx in .agents/skills/improve-codebase-architecture/SKILL.md and read by ahel’s review.

Look for architectural friction that a focused structural change could reduce. Favor improvements that shrink interfaces, concentrate related complexity, and make important behavior easier to test and understand.

Scope the investigation

Follow the user's named subsystem or pain point. Otherwise use recent changes, repeated maintenance friction, test difficulty, or dependency structure to select a useful area. Read relevant domain language and ADRs when present so recommendations respect intentional boundaries.

Use the codebase-design skill when its deep-module vocabulary will sharpen the analysis. Explore independently in parallel only when the repository is large enough to benefit and the lanes do not duplicate work.

Find evidence

Consider:

  • interfaces that expose nearly as much complexity as their implementations;
  • behavior spread across modules that change together;
  • dependency seams that leak internal details;
  • testing that bypasses the real call path because no stable seam exists; and
  • repeated wrappers or indirection that add little leverage.

Treat these as prompts for investigation, not violations. Apply the deletion test where useful: would removing a layer concentrate complexity behind a better interface, or merely move it elsewhere?

Present candidates

For each material candidate, cite the affected code and explain:

  • the observed friction;
  • the structural change being considered;
  • expected benefits and tradeoffs;
  • evidence that the seam is real rather than hypothetical;
  • test impact and migration risk; and
  • conflicts with existing decisions.

Rank recommendations by evidence and likely leverage. Keep speculative ideas clearly labeled and avoid proposing a wide refactor solely for aesthetic consistency.

Use prose, a compact table, or diagrams according to what best communicates the relationships. When a visual HTML artifact is useful and authorized, load the HTML report guide.

Explore a selected candidate further only when the user requests it or the current task includes design. Update domain records or ADRs only when those artifacts are in scope and the decision is durable enough to justify them.

Signals

GitHub stars
585
Forks
71
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
improve-codebase-architecture-stevesolun
Source
github.com/stevesolun/ctx