The pagerank of crypto is here, allowing any developer to provide contextual and personalized feeds that capture the ever-expanding universe of onchain and offchain data.

It's called OpenRank, and I've created an end-to-end example flow using their python sdk and Dune queries.

Launching the OpenRank SDK that allows developers to utilize the first version of OpenRank Protocol to create their own context-specific rankings and reputation scores on any input data set. Developers can bring onchain or offchain data and compute reputation in a self-serve way.

The SDK is already being used for ranking and reputation across onchain transactions, attestations, credentials, farcaster, lens and even off chain data sets such as GitHub forks, stars, PRs.

Use cases that can leverage the SDK include social graphs, github code collaborations, contribution attributions, attestations, tipping and so on. Check out our documentation here to learn more

Use cases that can leverage the SDK include social graphs, github code collaborations, contribution attributions, attestations, tipping and so on. Check out our documentation here to learn more
https://docs.openrank.com/openrank-sdk

Here is also a full walk-through example from Andrew Hong (@ilemi) using Dune data and OpenRank SDK: 
https://www.supercast.xyz/c/0x79b021aabcb0e2e6ff3a9a3e3be6788947f4eb8d
Get in touch to learn more about benefiting from OpenRank SDK for your reputation use case!

SDK v1 supports EigenTrust algorithm, which is most suitable for ranking and reputation based on any peer-to-peer data set or graph. For example, voting, p2p vouching and credentialing, social network engagement, token transfers.