For the last year, getting a coding agent to match your standards meant pasting the same instructions into every session, or maintaining a sprawling rules file that slowly drifted out of date. That workaround is over. Agents now load skills — self-contained bundles of instructions the model reads only when the task calls for them — and the ecosystem around them has grown quickly enough to need curation.
So we built one. listin.gg now has an Agent Skills section: 25 skills across interface design, development, motion, and research, each one checked, categorised, and installable in a single command.
What a skill actually is
A skill is a folder with a markdown file and some frontmatter. The frontmatter carries a name and a description; the description is the part that matters, because it's what the agent scans to decide whether the skill is relevant to what you just asked for. If it is, the full instructions load into context. If it isn't, they stay out of the way.
That distinction is the whole reason skills beat a rules file. A rules file is always loaded, so every instruction competes for attention on every task. A skill costs you nothing until the moment it applies. You can install twenty of them and pay for one.
“The interesting thing about skills isn't that they add capability — it's that they add capability conditionally. A well-written description is worth more than a well-written skill, because a skill that never loads at the right moment may as well not exist.”
— Tama
Why this needed a directory
Skills are distributed as GitHub repositories, which means discovery works exactly as badly as you'd expect. They surface through screenshots on X, a link in someone's newsletter, or a repo that trends for a day and then disappears. There's no equivalent of npm search, and the quality range is enormous — a genuinely good motion-review skill sits alongside a dozen that are three bullet points and a promise.
The other problem is attribution of quality. A repo's star count tells you how popular the repository is, not how good a specific skill inside it is. Several of the best skills we found live in monorepos with tens of thousands of stars earned by something else entirely. Others are single-purpose repos with a few hundred stars and sharper output.
What's in the list today
We've grouped the first 25 into four categories, weighted toward the work our readers actually hand to agents:
- Interface (13) — design critique, UI polish passes, accessibility audits, colour systems in OKLCH, and extracting a starting set of design tokens from a live site
- Development (8) — frontend design, shadcn/ui composition, React and Next.js performance rules, advanced TypeScript types, and persistent browser automation for agent workflows
- Motion (3) — Apple-style fluid motion translated to the web, animation code review, and a reverse-lookup glossary for naming the effect you're trying to describe
- Research (1) — structured interrogation of a plan before you commit engineering time to it
Each listing shows who publishes it, what it does in one line, the source repository's current star count fetched live rather than frozen at the time we wrote the entry, and a copy-to-clipboard install command. The row itself links through to the full skill documentation.
Installing one takes a single command
Every skill in the directory installs the same way. Hit the Install button on a row, paste, and you're done:
- npx skills add https://github.com/emilkowalski/skill --skill emil-design-eng
- npx skills add https://github.com/vercel-labs/agent-skills --skill web-design-guidelines
- npx skills add https://github.com/anthropics/skills --skill canvas-design
Skills are agent-agnostic in principle — they're just markdown — though support varies. Claude Code reads them natively. Cursor and other agents are converging on compatible formats, and a skill written for one generally transfers with little or no editing.
How to pick skills without bloating your agent
The failure mode we'd warn against is installing everything in a category because it's cheap to do so. Skills load conditionally, but their descriptions don't — the agent reads every description on every turn to decide what's relevant. Thirty installed skills means thirty descriptions competing to be matched, and the matching gets worse, not better, as the list grows.
A more useful approach: install the two or three that map to work you do weekly, use them for a fortnight, and only add more when you hit a task where you can name what's missing. Skills that overlap heavily are worse than either one alone, because the agent has to guess which you meant.
What we deliberately left out
This list is design and frontend weighted, and that's a choice rather than an oversight. There are good skills for data engineering, infrastructure, and security review, but we haven't used them enough to vouch for them, and a directory that lists everything is the problem we set out to solve rather than a solution to it.
We also left out skills that are mostly prompt boilerplate — the ones that restate what the model already does well, wrapped in enough markdown headers to look substantial. If a skill's instructions could be replaced by a single sentence in your prompt, it isn't earning its slot.
What's next
The list will grow, but slowly and with the same filter. If you maintain a skill you think belongs here, or you're using one daily that we've missed, send it over — the ones that make the cut are the ones someone can point at a specific piece of work and say it measurably improved the output.
Browse the full list at listin.gg/skills. It's free, there's no signup, and the install commands work whether you found them here or anywhere else.
