Skills Night: 69,000+ ways agents are getting smarter
Source: Vercel Blog
5 min read
Feb 20, 2026
Skills Night is heading to New York
Join us in New York for our next skills.sh event. See from developers how they’re using skills to make their agents smarter.
Where this came from
The origin story is worth retelling because it shapes how we think about the project.
Shu Ding is one of the most talented web engineers I’ve ever worked with. He knows things about React and the browser that most people will never discover. Last year he sat down for a weekend and wrote it all down—a kind of web bible. We wanted to figure out how to ship it. We considered a blog post or documentation that the next generation of models might eventually learn—but we wouldn’t see the results until Claude Sonnet 8, or GPT‑9. On the other hand, an MCP server felt too heavy for what was essentially a collection of Markdown documents.
Skills made sense as the quickest way to deliver on‑demand knowledge. While writing the instructions for installing React best practices, I kept copying the same installation steps for Cursor, Claude Code, Codex, and the other 10 + coding agents—each with slightly different installation directories.
So I built a CLI to install the skill into every major coding agent at once. That became npx skills. We added telemetry to surface new skills as they got installed, which became the data that powers the leaderboard at skills.sh. The whole thing went from idea to production on Vercel in days. Malte Ubl, Vercel CTO, framed it perfectly: it’s a package manager for agent context.
Now we are tracking 69 000 of them, and making them not just easy to discover but easy to install, with simple commands like:
npx skills add vercel-labs/agent-skills --skill vercel-react-best-practicesThe security problem we needed to solve
Growth creates attack surface, and fast growth creates it even faster.
As soon as skills took off, quality variance followed. Ryan from Socket showed us a concrete example: a skill that looked completely clean at the Markdown level but included a Python file that opened a remote shell on install. You would never catch that without inspecting every file in the directory.
That is why we announced security partnerships with Gen, Socket, and Snyk to run audits across all skills and every new one that comes in.
- Socket – cross‑ecosystem static analysis combined with LLM‑based noise reduction, reporting 95 % precision, 98 % recall, and 97 % F1 across their benchmarks.
- Gen – a real‑time agent trust layer called Sage that monitors every connection in and out of your agents, allowing them to run freely without risk of data exfiltration or prompt injection.
- Snyk – bringing their package‑security expertise to the skills context.
We are building an Audits leaderboard to provide per‑skill assessments and recommendations. The goal isn’t to lock things down; it’s to let you go fast with confidence. We’re always looking for new security partners who can bring unique perspectives to auditing skills and provide more trust signals for the ecosystem.
What the demos showed us
Eight partners showed demos on Tuesday, and a few themes kept coming up.
Skills close the training‑cutoff gap
Ben Davis ran a controlled experiment to demonstrate this. He tried to get coding agents to implement Svelte remote functions—a relatively new API—in four different ways:
- No context.
- A skills file with documentation.
- A skill pointing to the MCP.
- A code example in the project.
Every approach with context worked. The no‑context run, forced through a stripped‑down model to prevent it from inferring solutions, produced completely wrong output. Models are smart enough to use patterns correctly when you give them the patterns; without context they fall back to stale training data.
The medium matters less than the content
The interesting takeaway from Ben’s experiment wasn’t that skills are the only way; it’s that getting the right context in is what matters, and skills are the fastest starting point if you don’t already have a baseline. Existing code examples, inline documentation, and MCP hints all work. Skills are simply the easiest way to distribute that context to anyone.
Agents can now drive the whole stack
Evan Bacon from Expo showed native iOS feature upgrades driven entirely by Claude Code using Expo skills. New SwiftUI components, gesture‑driven transitions, and tab‑bar updates were all applied automatically. They are also using LLDB integration in a work‑in‑progress skill that lets agents read the native iOS view hierarchy and fix notoriously hard keyboard‑handling bugs automatically.
Their production app, Expo Go, now auto‑fixes every crash as it occurs. For anyone who has spent time wrestling with Xcode, that is a significant statement.
Skills are becoming infrastructure
Nick Khami showed that Mintlify auto‑generates a skill for every documentation site they host, including Claude Code’s own docs, Coinbase, Perplexity, and Lovable. Traffic to these…
Skills are now 50 % coding agents, up from 10 % a year ago
The skill is not something the docs team writes anymore; it is a byproduct of having well‑structured documentation.
Sentry’s David Cramer built Warden, a harness that runs skills as linters on pull requests via GitHub Actions, treating agents as a static‑analysis layer.
What we’re building toward
Guillermo Rauch, Vercel CEO, said something Tuesday night that I keep thinking about: agents make mistakes.
They sometimes tell you you are absolutely right and proceed to do the wrong thing.
Shipping quality in the AI era means not just celebrating how many tokens you are burning. It means raising the bar on what those tokens actually produce.
Skills are one answer to that problem. They are how we:
- Influence what agents create
- Keep agents up to date with framework changes
- Make agents more token‑efficient by giving them a straight path to the right answer instead of letting them stumble around
Two million installs is real signal. The security partnerships make it something teams can rely on. And the demos showed that the most interesting skills work is not at the CLI level—it’s in the agents and tools that now treat skills as a first‑class primitive for distributing knowledge at scale.
We will keep building. Come find us at skills.sh.
Skills Night is heading to London
Join us in London for our next skills.sh event. Hear from developers how they’re using skills to make their agents smarter.