Finding skills worth running

Someone on r/ClaudeAI spent a full week testing 30+ community skills to figure out which ones were any good. The post hit 677 upvotes, because everyone has the same problem and no one has a better method.

That's the state of skill discovery today. There are over 900,000 public skills on skills.sh alone. Install counts and stars don't tell you which ones work. So you either install blind, or you do what that guy did and burn a week testing.

Here's how to do better.

The catalog can't tell you what's good

A giant list sorted by downloads measures popularity, not quality. Worse, most of the catalog is noise. One of the top replies on that thread was a joke that stuck: "All posts about AI must include the term game-changing." Every skill claims to change your life. Most wrap something the model already does.

Ranking by installs surfaces the loudest, not the best. You need a different signal.

The signal that works: who stands behind it

The fastest way to find a good skill is to stop browsing catalogs and start following people. When someone whose taste you already trust runs a skill, that's worth more than 10,000 anonymous installs.

This is how you already find everything else. You don't pick a restaurant by counting how many people walked in; you ask someone whose judgment you trust. Skills are the same: trust the person, not the catalog.

So:

• Follow a few people whose work you respect (in your stack, your role, your team). Watch what they actually run.
• Weight "people I trust use this" over raw install counts. One trusted user beats a thousand strangers.
• Treat your team as the best filter. If three teammates already run a skill, that is real, earned signal, not a number you have to guess at.

A 60-second vet before you install

When you do try a new skill, you can rule most out fast. Open the SKILL.md and check:

• Specific trigger. A real description says "Use when reviewing TypeScript PRs for type safety." A bad one says "helps with code." Vague trigger means it fires at the wrong time or never.
• Does one thing. A skill that claims to handle your whole workflow will load constantly and help rarely.
• Instructions, not a README. Good skills read like a briefing for a sharp new teammate. If it's marketing copy or a pasted README, skip it.
• No surprises in the body. Scan for anything that touches secrets, runs shell commands, or reaches out to the network you didn't expect.

The repo hosting the skill gets the same treatment, because star counts get farmed and lists get gamed:

• Author before repo. A handful of followers behind hundreds of stars is a tell, not an anomaly.
• Curve before count. A real spike has a visible cause you can click: a launch tweet, a Show HN, a video. Stars with no trail behind them were probably bought.
• Check the issues. A thousand stars and zero questions ever asked rarely means a thousand users.

A minute of reading kills most candidates. The ones that pass are worth a real try.

The whole method fits in three sentences. Install counts measure noise, not quality. The durable signal is human: follow people whose taste you trust, and weight what your team already runs. Before installing, read the SKILL.md for a specific trigger, one job, real instructions, and no surprises.

How Skillet helps

This is the whole reason Skillet leads with people, not a catalog. You follow the people whose skills are worth running, subscribe to their collections, and their best work shows up in your tools and stays current. Skill pages show whether anyone you follow already uses something, so "who do I trust" is answered before you install, not after a week of testing.

Start by following one person whose work you respect:

npx skillet

Or browse from the people you trust in the app.

Trust the person, not the catalog.