automodellisted

# automodel A structured guide to discovering models from data with agents. Focuses on the structure of the model (be it equation terms or neural network layers) rather than just parameter values, using an iterative meta/inner agent loop to explore structural modifications in parallel. Applies to a wide range of tasks, from simple regression to complex physical systems, from automotive to pharmacology. ## Phases This skill is organized into four sequential phases, each with a detailed recipe in `assets/phases/`: | Phase | Recipe | Entry signal | |---|---|---| | **1 — Setup** | `assets/phases/1_setup.md` | No `CONTEXT.md` artifact in project root | | **2 — Baseline Model** | `assets/phases/2_baseline_model.md` | `CONTEXT.md` exists; Some prep work done; | | **3 — Iterate** | `assets/phases/3_iterate.md` | End-to-end pipeline verified for baseline model (resume from the highest existing `meta_*/` directory) | | **4 — Finalize** | `assets/phases/4_finalize.md` | User satisfied with validation performance; `CONTEXT.md` points to best model | Each phase updates `CONTEXT.md`. Use the resulting artifacts as re-entry signals when resuming. ## How to start 1. Copy `assets/CHECKLIST.md` to the project root (if not already present) and update it as you go. 2. Detect the current phase using the entry signals above. If a checklist with partial progress is already present, ask the user whether to resume. 3. Read **only the frontmatter** of all phase files to confirm which one applies