OracleLens
Evaluate the credibility of the oracle data
Created on 1st February 2026
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OracleLens
Evaluate the credibility of the oracle data
The problem OracleLens solves
OracleLens is NOT an oracle — it sits between oracle data and on-chain decisions to verify data credibility.
What it does:
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Evaluates oracle data using the S.T.A.P. scoring system (Source, Time, Accuracy, Proof)
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Provides zkTLS verification via Primus Labs SDK for cryptographic data authenticity
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AI-powered formula selection for different data types
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On-chain submission of credibility scores
Use cases: Smart contracts, DAOs, prediction markets that need to verify oracle data before making critical decisions.
Challenges I ran into
At first, I didn't know what project to undertake. However, after reading Vitalik's interview, he mentioned the incident of the prediction market in Ukraine, which made me realize that there are some trust issues in the current oracle system. Once the source of the data is compromised, the blockchain actually cannot determine whether the data is trustworthy. Most contracts on the blockchain will blindly believe the data provided by the oracle, and this project attempts to solve this problem by allowing the blockchain to make an independent judgment after obtaining the oracle data, whether to directly use the data from the oracle or not.
Of course, I know this demo is not perfect and cannot be directly applied to the production environment, but it provides a way to solve the problem.
I encountered some issues when connecting to PrimusLabs. For instance, I couldn't select the data I wanted for zk proof. However, I eventually resolved this problem by watching their demonstration video.
Tracks Applied (1)
Overall Awards
Technologies used
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