dsai

Scaffolds short-lived R or Python scripts that call Ollama Cloud /api/chat with tool definitions, batch rows or records per request, optionally parallelize chunk calls (e.g. furrr), and apply tool results on the main process with JSONL audits—using a folder-local .env. Use when the user wants an agent loop for data cleaning, enrichment, geospatial routing, dataset compilation, batched tool calls, parallel Ollama chunks, or teaching patterns aligned with 10_data_management/fixer (not full Plumber/FastAPI agents unless requested).

Adds clear console progress and status output for R and Python scripts using section dividers, Unicode emoji icons, dimensions (nrow/length), paths, and previews so runs are debuggable from logs pasted into AI chats. Use when polishing scripts, improving observability, or when the user asks for progress messages, status lines, or emoji console UX.

Write elegant modern tidyverse R for this course repo—always = for assignment (never <-), dplyr 1.1+ (join_by, .by), native pipe, explicit library() per file, vectorized mutate/case_when, httr2 and purrr possibly/safely, rlang data-mask patterns. Use when editing 03_query_ai, 10_data_management, fixer, or refactoring API/data pipelines.