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Claude Code / Codex session metadata can actually tell a story about how you work with AI coding agents. 50 days ago we posted about analyzing 1.6k Claude Code sessions from our own team. Skills were used in 4% of sessions, 26% were abandoned early, and we had no real benchmark for what good looked like. Now across 20k+ sessions, we started looking at behavior patterns from derived session metadata: consistency, intensity, session shape, repo breadth, output, cost intensity, and model range. Nine archetypes fell out, which we turned into playful cards. We built a Spotify Wrapped meets trading cards experience on top of the classifier: upload your sessions and get a story about how you use AI coding agents. The classifier runs on metadata like duration, token counts, model mix, repo count, and commit rates. Create yours today. It’s free an

file content is also be uploaded as well https://github.com/obsessiondb/rudel?tab=readme-ov-file#secu... if you dont trust us with that data though (which i can understand) you can host that thing locally on your machine

The docker-compose contain everything you should need: https://github.com/obsessiondb/rudel/blob/main/docker-compos...

We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time. So we built an analytics layer for it. After connecting our own sessions, we ended up with a dataset of 1,573 real Claude Code sessions, 15M+ tokens, 270K+ interactions. Some things we found that surprised us: - Skills were only being used in 4% of our sessions - 26% of sessions are abandoned, most within the first 60 seconds - Session success rate varies significantly by task type (documentation scores highest, refactoring lowest) - Error cascade patterns appear in the first 2 minutes and predict abandonment with reasonable accuracy - There is no meaningful benchmark for 'good' agentic session performance, we