Hi Mark!

@Brett Saviano is right that client-side editing is the cleanest long-term answer — if you can get your code on disk, do that, and Claude Code works great with local files. But I know there are cases where that's not practical: large server-side codebases with existing source control hooks, production systems where a sync workflow would be disruptive, or legacy .mac routines that were never meant to leave the server.

For those cases, @Ernest Salas's MCP server looks like a great solution -- worth starting there if you want a full MCP setup.

If you want something even lighter — no Node.js install, no server to run — there's a zero-dependency option. The Atelier REST API is just HTTP, and you can hand Claude Code two markdown skill files that tell it how to use curl directly. Nothing to install beyond what's already on your machine:

mkdir -p .claude/skills/compile .claude/skills/introspect

curl -sL "https://gist.githubusercontent.com/isc-tdyar/31061a6024ab104d46c470a93ae4962a/raw/compile.md" \
  > .claude/skills/compile/SKILL.md

curl -sL "https://gist.githubusercontent.com/isc-tdyar/31061a6024ab104d46c470a93ae4962a/raw/introspect.md" \
  > .claude/skills/introspect/SKILL.md

Then tell Claude your IRIS web port (default 52773, or docker port <container> 52773 for Docker) and it handles the rest — reads source, writes back, compiles, returns errors. The skills cover the 409 / ?ignoreConflict=1 pattern so Claude doesn't get confused by Atelier's concurrency check.

I tested it on IRIS 2025.1 with Claude Sonnet 4.6 on a .mac routine — read, identified bugs, fixed, compiled clean in one pass with the skill loaded. Without the skill, it took four extra round-trips guessing at API versions before converging.

Gist: https://gist.github.com/isc-tdyar/31061a6024ab104d46c470a93ae4962a

Congratulations to the winners! But also congratulations to all the contestants, it was really difficult voting since there were so many great applications!!

Thanks for sharing Suze and team BAS!

Very cool, thanks for sharing, Robbie!

Excellent, I love how this is end-to-end and using Interoperability!!

Andrey, we only support H2O as a Provider in the InterSystems IRIS Cloud IntegratedML service. Kinshuk has pointed out that you can make different H2O configurations, each with different default settings you can set with "USING".

But I think the problem is with your data.

Although I haven't seen that error, and the message is not very helpful, the beauty of open source is that you can see (a newer version of) the code for that method "inferDistribution" here -- the code is analyzing the response (target) column and looking at the statistics of the categories. In your case, it only found one category, and needs to find 2 or more.

Please check the data and see if you have reasonable values in your target column. If so, I am happy to dig in further with you and resolve the issue :)

Regards,
Thomas