Track and search download history across 70,000+ agent skills with daily snapshots. Built for developers evaluating skill adoption trends and monitoring performance metrics over time.
io.github.pineapple-farm/skill-history
https://skill-history.com/mcp
HTTP
No auth required
How models use it and what it is built for.
Track and search download history across 70,000+ agent skills with daily snapshots. Built for developers evaluating skill adoption trends and monitoring performance metrics over time.
Hosted endpoint — paste into any MCP client.
Where to find authoritative docs and source for Skill History.
Paste any of these into Agent Studio after connecting Skill History.
Common questions about connecting and running Skill History.
What data does this server track?
It maintains download history for over 70,000 agent skills with daily snapshots, allowing you to query historical trends and current adoption metrics. You can search skills by name or category and retrieve time-series download data.
How far back does the download history go?
The registry metadata specifies daily snapshots are available, but the exact retention period isn't documented. Check the hosted endpoint documentation at https://skill-history.com/mcp for specific date ranges supported.
Can I filter skills by category or tag?
The server supports searching across 70,000+ skills, though the exact filtering options aren't detailed in the registry. Refer to the API documentation to see available search parameters and category taxonomies.
Is there rate limiting or authentication required?
The server uses HTTP transport but authentication requirements aren't specified in the registry metadata. Contact the maintainer or check https://skill-history.com/mcp for API key requirements and rate limits.
What's the difference between this and manually checking individual skill pages?
This MCP server aggregates historical data across thousands of skills in one query and provides daily snapshots for trend analysis, whereas individual pages typically show only current stats. It's designed for bulk analysis and time-series research.
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