Show HN: Rudel – Claude Code Session Analytics

Try this: Keep your CLAUDE.md as simple as possible, disable skills, and request Opus to start a subagent for each of the files and process at most 10 at a time (so you don't get rate limited) and give it the instructions in the skill for whatever processing you're doing to the markdowns as a prompt, see if that helps.

https://scottspence.com/posts/measuring-claude-code-skill-ac...

This works in my experience

yes we had to tune the claude.md and the skill trigger quite a bit, to get it much better. But to be honest also 4.6 did improve it quite a bit. Did you run into your issues under 4.5 or 4.6?

Try the latest skill-creator, has a/b testing

insights is straight ego fluffing - it just tells you how brilliant you are and the only actionable insights are the ones hardcoded into the skill that appear for everyone. things like be very specific with the success criteria ahead of time (more than any human could ever possibly be), tell the llm exactly what steps to follow to the letter (instead of doing those steps yourself), use more skills (here's an example you can copy paste that has 2 lines and just tells it to be careful), and a couple of actually neat ideas (like having it use playwright to test changes visually after a UI change)

Ohh this is exciting, I kinda overlooked it. I assume there are still a lot of differences, especially for accross teams. But I immediately ran it, when I saw your comment. Actually still running.

true, the best comes out of it when one uses claude code and codex as a tag team

this jumped out at me too. What counts as "abandoned"? How do you know the goal was not simply met?

I have longer threads that I don't want to pollute with side quests. I will pull up multiple other chats and ask one or two questions about completely tangential or unrelated things.

I agree. In my experience: "single-purpose sessions for small tasks" is the key

Note: Linko currently only supports macOS.

interesting, where did you see that?

Its our own sessions, from our team, over the last 3 months. We used them to develop the product and learn about our usage. You are right, they will remain closed. But I am happy to share aggregated information, if you have specific questions about the dataset.

it's reasonable to note that w/o sharing the data these findings can't be audited or built upon

but i think the prior on 'this team fabricated these findings' is v low

Ah sorry, the readme is more about how to run the repo. The "product" information is rather on the website: https://rudel.ai

Our focus is a little bit more cross team, and in our internal version, we have also some continuous improvement monitoring, which we will probably release as well.

> A local-first desktop and web app for browsing, searching, and analyzing your past AI coding sessions. See what your agents actually did across every project.

Thx for the link - sounds great !

Cool, whats the link? We have some learnings, especially in the "Skill guiding" part of our example.

yup!

We used it across the team, and when you want to bring metrics together across multiple people, its easier on a server, than local.

Hey, here is Rafa, another Rudel AI developer. The ultimate goal is to make developers more productive. Suddenly, we had everyone having dozens of sessions per day, producing 10X more code, we were having 10X more activity but not necessarily 10X productivity.

With this data, you can measure if you are spending too many tokens on sessions, how successful sessions are, and what makes them successful. Developers can also share individual sessions where they struggle with their peers and share learnings and avoid errors that others have had.

yes what rafa said... aaand we see who wastes the 200 bucks claude subscription by not using it

The main reason we've seen is a bad first impression: the agent misunderstands the initial prompt, doesn't load the available tools or starts doing something clearly wrong, and the user ctrl+c's instead of correcting it since it's faster to just start a new one.

The learning is that it is fixable. Better CLAUDE md instructions, clearer initial prompts, and skill configurations that reduce the uncertainty cut abandonment significantly in our team.

evren here from the rudel team. and that's a great question.. i genuinely dont know. let me find out

It will be only enabled for the repo where you called the `enable` command. Or use the cli `upload` command for specific sessions.

Or you can run your own instance, but we will need to add docs, on how to control the endpoint properly in the CLI.

you can host the whole thing locally :)

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 skill usage was one of these "I am wondering about...." things, and we just prompted it into the dashboard to undertand it. We have some of these "hunches" where its easier to analyze having sessions from everyone together to understand similarities as well as differences. And we answered a few of those kinda one off questions this way. Ongoing, we are also using a lot our "learning" tracking, which is not really usable right now, because it integrates with a few of our other things, but we are planning to release it also soon. Also the single session view sometimes helps to debug a sessions, and then better guide a "learning". So its a mix of different things, since we have multiple projects, we can even derive how much we are working on each project, and it kinda maps better than our Linear points :)

Team of 4 engineers, 1 data & business person, 1 design engineer.

I would say roughly equal amount of sessions between them (very roughly)

Also maybe 40% of coding sessions in large brownfield project. 50% greenfield, and remaining 10% non coding tasks.

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.

I was about to say they have a self-hosting guide, but I see they use third party services that seem absolutely pointless for such a tiny dataset. For comparison, I have a project that happily analyzes 150 million tokens worth of Claude session data w/some basic caching in plain text files on a $300 mini pc in seconds... If/when I reach billions, I might throw Sqlite into the stack. Maybe once I reach tens of billions, something bigger will be worthwhile.

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

1. can only partly be answered, because we can only capture the "edits" that are prompted, vs manual ones. 2. for us actually all of them, since we do everything with ai, and invest heavily and continously, to just reduce the amount of iterations we need on it 3. thats a good one, we dont have anything specific for debugging yet, but it might be an interesting class for a type of session.

To clarify, our data set consists solely of Claude Code sessions, specifically those with a human behind them. Rudel AI, in its current form, focuses on "How teams code with AI". We have plans to expland to a larger range of agentic observability use cases.

What tools do you use to run your analysis?

Can you expand on the USDC friction piece?

This is great. How are you "identifying" these stages in the session? Or is it just different slash commands / skills per stage? If its something generic enough, maybe we can build the analysis into it, so it works for your use case. Otherwise feel free to fork the repo, and add your additional analysis. Let me know if you need help.

This is a great idea, documented and added to our roadmap.

I usually instruct the agent to use the skills explicitly, e.g. "/writing-tests write the tests for @some-class.cpp"

So the skills are mostly a sort of on-demand AGENTS.md specific to the task.

Another example is I have a `plan-review` skill, so when planning something I add at the end of the prompt something like: "plan the task, .... then launch claude and codex /plan-review agents in parallel and take their findings into account before producing the final plan".

The 4% usage was about our internal team, and we have skills setup. So it is not necessary that they are not built, but rather that they were not used, when we expected them to be used. So we adapted our CLAUDE.md to make claude more eager to use them. Also the 4% usage was on the 4.5 models, 4.6 got much better with invoking skills.

It's crazy how fast I'm able to identify these bots now. You just get an uncanny valley type of feeling immediately reading it. Sure enough you click the profile and it's a brand new account with one or two similar posts in the same style. There's some sort of writing style here that identifies it because I've picked upon it multiple times quickly but it's hard to articulate into words.

Heavy use of /rewind helps with this - it's much better to remove the bad information from the context entirely instead of trying to tell the model "actually, ignore the previous approach and try this instead"

> The 26% abandonment rate, the error cascade patterns in the first 2 minutes — these are behavioural signals, not just performance metrics.

> When Claude Code gets stuck in a loop, tries an unexpected tool chain, or produces inconsistent outputs under adversarial prompts — those aren't just UX failures, they're security surface area.

Twice in one paragraph, not even trying to blend in.

LLM comment spotted

The tool does have a quite detailed view for individual sessions. Which allows you to understand input and output much better, but obviously its still mysterious how the output is generated from that input.