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 and open source: https://app.rudel.ai/wrapped
Repo: https://github.com/obsessiondb/rudel
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 and open source: https://app.rudel.ai/wrapped
Repo: https://github.com/obsessiondb/rudel