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Show HN: Rudel – Claude Code Session Analytics (github.com)

144 points by keks0r ↗ HN
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 are building one.

The tool is free to use and fully open source, happy to answer questions about the data or how we built it.

47 comments

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lau_chan
Does it work for Codex?
keks0r
Yes we added codex support, but its not yet extensively tested. Session upload works, but we kinda have to still QA all the analytics extraction.
marconardus
It might be worthwhile to include some of an example run in your readme.

I scrolled through and didn’t see enough to justify installing and running a thing

152334H
is there a reason, other than general faith in humanity, to assume those '1573 sessions' are real?

I do not see any link or source for the data. I assume it is to remain closed, if it exists.

mrothroc
Nice, I've been working on the same problem from a different direction. Instead of analyzing sessions after the fact, I built a pipeline that structures them. Stages (plan, design, code, review, same as you'd have with humans) with gates in between.

The gates categorize issues into auto-fix or human-review. Auto-fix gets sent back to the coding agent, it re-reviews, and only the hard stuff makes it to me. That structure took me from about 73% first-pass acceptance to over 90%.

What I've been focused on lately is figuring out which gates actually earn their keep and which ones overlap with each other. The session-level analytics you're building would be useful on top of this, I don't have great visibility into token usage or timing per stage right now.

I wrote up the analysis: https://michael.roth.rocks/research/543-hours/

I also open sourced my log analysis tools: https://github.com/mrothroc/claude-code-log-analyzer

ekropotin
> That's it. Your Claude Code sessions will now be uploaded automatically.

No, thanks

emehex
For those unaware, Claude Code comes with a built in /insights command...
vova_hn2
This is so sad that on top of black box LLMs we also build all these tools that are pretty much black box as well.

It became very hard to understand what exactly is sent to LLM as input/context and how exactly is the output processed.

KaiserPister
This is awesome! I’m working on the Open Prompt Initiative as a way for open source to share prompting knowledge.
alyxya
Why does it need login and cloud upload? A local cli tool analyzing logs should be sufficient.
anthonySs
is this observability for your claude code calls or specifically for high level insights like skill usage?

would love to know your actual day to day use case for what you built

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Aurornis
> 26% of sessions are abandoned, most within the first 60 seconds

Starting new sessions frequently and using separate new sessions for small tasks is a good practice.

Keeping context clean and focused is a highly effective way to keep the agent on task. Having an up to date AGENTS.md should allow for new sessions to get into simple tasks quickly so you can use single-purpose sessions for small tasks without carrying the baggage of a long past context into them.

dmix
I've seen Claude ignore important parts of skills/agent files multiple times. I was running a clean up SKILL.md on a hundred markdown files, manually in small groups of 5, and about half the time it listened and ran the skill as written. The other half it would start trying to understand the codebase looking for markdown stuff for 2min, for no good reason, before reverting back to what the skill said.

LLMs are far from consistent.

longtermemory
From session analysis, it would be interesting to understand how crucial the documentation, the level of detail in CLAUDE.md, is. It seems to me that sometimes documentation (that's too long and often out of date) contributes to greater entropy rather than greater efficiency of the model and agent.

It seems to me that sometimes it's better and more effective to remove, clean up, and simplify (both from CLAUDE.md and the code) rather than having everything documented in detail.

Therefore, from session analysis, it would be interesting to identify the relationship between documentation in CLAUDE.md and model efficiency. How often does the developer reject the LLM output in relation to the level of detail in CLAUDE.md?