AI Guides › Skills & Agents

Your Skill Library Is A Codebase Now — Treat It Like One

By Nigel Guy · 2 min read

Once you have more than a handful of skills, you have something that behaves like software, whether or not you think of it that way: files that depend on external tools, that can go stale, that interact with each other, and that someone (probably future you) will need to understand without the context you have right now.

The rule: once your skill collection grows past a handful, apply the same basic discipline you'd want from any codebase — naming, versioning, and documentation — rather than letting it stay a pile of individually reasonable files.

The mechanism

  1. Name skills so their purpose is obvious without opening them. A consistent naming pattern saves real time the moment you have more than ten of these.
  2. Note when each one was last verified working, somewhere you'll actually see it. This turns "I think this still works" into something checkable.
  3. Document what each skill assumes about its environment or connected tools. When something breaks later, this is what tells you where to look first.
  4. Treat a change to a shared dependency as a reason to re-check every skill that touches it — the same way a shared library update in real software prompts a regression check.
  5. Retire deliberately, don't just abandon. A skill nobody's touched in a year should be explicitly archived or deleted, not left in an ambiguous state where nobody's sure if it still works.

What to skip

Skip building elaborate tooling around a handful of skills — this discipline earns its keep once the collection is large enough that you can't hold it all in memory, not before. And skip treating documentation as optional because "it's just a skill, not real code" — the maintenance burden is real regardless of what you call it.

Guardrails

All 751 AI guides · JulieMango plans from £17/mo