I counted every skill my AI invoked across 55 sessions. Here's the leaderboard.

I had over a hundred skills installed while building ParkEase. Which ones actually pulled their weight?

Instead of guessing, I counted. Claude Code keeps a transcript of every session, so I searched all 55 ParkEase sessions (10 September to 19 October) for every time the agent explicitly invoked a skill.

One caveat up front: this counts explicit invocations only. A skill already loaded keeps shaping the work without being re-invoked, so these are "times it was called on", not "times it mattered".

Behind those: test-driven development, archify (architecture diagrams), executing plans and code review.

The router came first. flow doesn't write a line of code. It decides which other skills run. That it topped the list says something: the most-used tool in an AI workflow was the one that sets up the workflow.

Design skills outnumbered code skills. impeccable, ui-ux-pro-max and mobile-app-design together: 30 invocations. A code review can tell you code is wrong; it can't tell you a screen looks generic. The design skills existed because the AI's default UI output was competent and forgettable.

The boring safety net ranked high. Ten migration reviews. Every database migration was checked for things that lock a production table or break running code. No migration review ever made a demo exciting, which is exactly the point.

test-driven-development ranked low by count, high by effect. Only 4 explicit calls, because it was loaded once per build phase and stayed loaded. Its output, 3,246 unit tests and 1,080 integration tests, is the clearest proof that invocation counts undersell the skills that stick.

Separate from skills are subagents: single-lens reviewers the agent can dispatch. Ranked by dispatches, the specialists that ran most were the security reviewer, the silent-failure hunter (swallowed errors, empty catches, lost stack traces), the TypeScript reviewer, the test-coverage analyzer and the database reviewer.

The silent-failure hunter is the one I'd recommend to anyone. AI-written code is very good at "handling" an error by catching it and doing nothing, and a lens that only looks for that finds things the general reviewers skim past.

Everything else is situational. And more isn't better: post 13 is about being offered 292 skills and installing 21.

Next: a practical guide. How to get more out of AI-assisted development, from the habits that made the biggest difference here.

ParkEase is a peer-to-peer parking marketplace for India, built solo with Claude Code. Code: https://github.com/Deonkar/parkease