askLLM 1.3.0 — two analyses: jamovi Module Guider + R code tutor

askLLM 1.3.0 splits the module into two analyses, both under Analyses ▸ askLLM.

jamovi Module Guider

"Which jamovi analysis should I run?" — recommends the right analysis and quotes the exact menu path, grounded in the modules you actually have installed. If you ask for R code, it points you to R code tutor.

R code tutor (new)

"How do I write the R for this?" — writes R that works on your dataset for you to paste into the Rj Editor and run yourself (the tool teaches, you execute). Grounded in the R packages actually bundled with your Rj environment, so it only uses what you have — it never suggests install.packages(), read.csv(), or file paths. Three personas: Consultant (a ready snippet), Tutor (a # TODO skeleton), Explainer (line-by-line comments).

Rj Editor is a module for the desktop version of jamovi (not jamovi Cloud), so R code tutor needs desktop jamovi.

askLLM is your copilot, not an autopilot

Both analyses only advise: analysis strategies, jamovi menu paths, and R code you can paste into the Rj Editor. You run the analysis, you write to any column, you drive the jamovi interface — askLLM does none of that for you. This is now a stated principle, not just a description of the current build.

For a multi-step analysis, iterate like this: summarise the previous step's results in your next question, and the LLM builds its suggestion for the next step on that. askLLM does not read jamovi's analysis output (a platform limitation, not a missing feature), so you are the one who carries each result back.

Also new

• Learn R with Rj — a bilingual (EN/中文) guide page: https://scgeeker.github.io/askLLM/learn-r.html
• Each analysis's results link to the learn-R guide and a model picker.
• Bidirectional prompt boundaries: each analysis stays in its lane and redirects off-topic questions to the other.
• The experimental "actions" mode was removed from the UI (agent-style acting belongs in an upstream automation API, not an analysis module); its code is retained, dormant. Under the copilot boundary above, that path either stays dormant or, if it is ever re-wired, must still leave execution to the user.

Privacy (unchanged, by design)

Only summary statistics of the variables you pick are sent — never raw data rows — plus your question and (for R code tutor) the names of your Rj packages. askLLM never runs R for you. With a local model (Ollama), nothing leaves your machine.

Install

Download askLLM_1.3.0_win64_jamovi-2.7.jmo below → in jamovi, ⋮ (top-right) ▸ modules ▸ install from file / sideload. Bring your own API key (NVIDIA / Gemini / OpenRouter / GitHub Models / Ollama / custom); see the in-app help and https://scgeeker.github.io/askLLM/choose-model.html

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Notes updated 2026-09-10: added the copilot boundary statement, and corrected a documentation error in LIMITATIONS where an illustrative example of hallucination had been described as a recorded test result. The module itself is unchanged from the 1.3.0 build below.