Building an Automated Token Discovery Pipeline with GPTs

[johnblockbuster]

Why stop at one-off prompts when you can build a data-driven scanner?

Create embeddings from white papers, social posts and GitHub commits. Add tokenomics risk scores (supply, vesting, liquidity), plus anomaly detection for large transfers or contract interactions.

Then cluster your metrics to surface unusual projects worth a closer look. Backtest your signals against historical data to refine your model.

Want more firepower? Explore multiple GPTs in your workflow — one for token safety, another for on-chain wallet tracking, another for research summaries. Combine them for a 360° view of a project.

This turns scattered data into a repeatable process — giving you a systematic way to spot high-potential tokens early.

[kelson10]

A scanner with embeddings + tokenomics risk scores would make it easier to spot strong vs risky projects.

[jacson4]

Adding anomaly detection for transfers/contracts turns it into a real-time early warning system.