Install
https://github.com/linny006/trending-claude-skillsWho it's for
If you want to quickly see which claude-skills, MCP servers, and AI agent capability repositories have been showing up actively recently, and you don't want to maintain a list yourself, this repository is a ready-made entry point. It positions itself as an automatically updated leaderboard: it queries the GitHub Search API, finds AI coding-agent skill repositories that were recently created or recently updated, sorts them by freshness and momentum rather than by cumulative star count, and writes the results into a self-updating leaderboard table in its README.
It suits three kinds of users: people doing tool selection who need to quickly scan for newly appearing agent capabilities; people who write Claude Code skills or MCP services themselves and want to see what comparable projects have been doing recently; and people who want a stable address to record an observation entry point with, instead of searching by hand again and again.
What it is not suitable for should be clear up front as well: it is a leaderboard, not a directly installable skill package, and the repository itself has no installation steps that install any of its entries into a client.
Your first attempt in 3 minutes
There is only one official path: open the repository's home page and read the leaderboard table in the README.
https://github.com/linny006/trending-claude-skills
There is only one prerequisite: being able to reach GitHub and view the README on the repository's home page. No client needs to be installed, and no files are written to your disk - the whole action is a read-only access.
Concrete steps:
- In the agent session you use, which is able to read web pages, make one read-only request, for example: "Read the leaderboard table in this repository's README on the home page and list the top 5 entries in order together with their repository URLs."
- Confirm that this request is pure reading: it writes no local files and changes nothing in the repository.
- Compare the returned entry names against the table on the repository's home page one by one.
There are three success signals: the returned entries match the table on the repository's home page one by one; the order is consistent with what you saw when you opened the page; and after the operation, no new files have appeared in the working directory. If the agent answers "I cannot access the web", or hands you a list of skills it recalled from memory, this attempt did not work.
The page also mentions that starring this repository makes it a bookmark, so you can return to the same entry point later. That is the only write operation the page documents, but it lands on your GitHub account rather than on local disk, so it is still worth confirming before you do it.
Use cases
Case one: discovering AI agent capabilities for video creation and social media
The scope of entries the leaderboard tracks includes Claude Code skills, MCP servers, and AI agent capabilities aimed at scenarios like video creation and social media management. Concretely, the operation is to have the agent read the leaderboard table on the repository's home page once, filter out the entries related to video creation or social media management, and list their repository URLs and current ranking positions.
The basis for this flow is this: the leaderboard queries GitHub Search API for skill-type repositories that were recently created or updated, and sorts by freshness and momentum; every refresh reflects real changes upstream, with new entries joining and expired entries being removed. So an entry in the same direction that survives several consecutive refreshes usually means it is still being updated - and that judgment layer makes use of exactly its volatility.
Case two: turning the leaderboard into a fixed observation entry point
The README explains that the table below is fully rewritten on every scheduled refresh, and gives a suggestion: star this repository to bookmark it. The corresponding operation is: once you have confirmed that you really do want to track this leaderboard over the long term, star the repository; from then on, visiting the repository's home page returns you to the same observation entry point.
The boundary needs to be spelled out too: a star saves an entry point, not a snapshot of content. The table can be completely different between two visits, so any citation should carry the meaning "this is the state at one particular read", instead of treating it as a stable dataset.
Limits of use
- The leaderboard is volatile content: the table is fully rewritten on every scheduled refresh, so any single read is only a slice of that moment, and no copy is kept locally automatically.
- Freshness is limited by the GitHub Search API search index; the repository itself labels its data source as GitHub Search API, so "it did not show up" does not mean "it does not exist".
- Ordering is based on freshness and momentum, not cumulative star count, so stable but long-abandoned older projects can rank further back.
- The repository only provides the leaderboard and no installation steps for any entry; whether an entry is usable or safe must be judged by the description in its own external repository.
- For long-term tracking, use a bookmarking method such as starring; do not count on copying page content and treating it as a fixed reference.