
What happened
hotskills.dev tracks over 6,400 repositories and about 84,000 unique skills; of 849 popular repos checked, 77.6% use network calls or broad shell access, 12.3% have local scripts, and 10.1% are instructions only.
Why it matters
The site's stated purpose is to show what each skill executes before installation, since skills are often installed without inspection; 77.6% needing network or broad shell access suggests many are not self-contained, so reviewing content before install has clear value.
Summaries like this, every morning.
The project emerged from a simple gap: npm packages are often inspected before installation, but AI agent skills tend to be installed as-is. To address that, the developer collected candidates from GitHub code search, topic search, and awesome lists, judging whether a repository distributes skills from its file tree alone without using an LLM; git clone with --depth 1 --filter=blob:none --no-checkout fetches only the file list. Skills are counted by hashing the content of SKILL.md rather than by file count, and star counts are refreshed hourly via GitHub's GraphQL API.
The data also covers context cost. Across 52,571 unique skills, frontmatter that is always loaded into context has a median of 114 tokens, while the SKILL.md body loaded when a skill activates has a median of 1,353 tokens, with the top 25% at 2,394 tokens or more. That means 10 installed skills consume roughly 1,100 tokens before any input, and per-skill estimate pages let users choose lighter ones. Growth has been fast: in October, stars on skill-related repositories rose by 987,550 (+12.0%) and 513 new repositories appeared.
To address the fact that skills can change after installation, the install commands hotskills presents are pinned to checked commits. A read-only MCP server is also published on the official MCP Registry (dev.hotskills/hotskills), returning rankings, security signals, and commit-pinned install commands for free.
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