Unlayer-AI 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
- Copy
assets/CHECKLIST.mdto the project root (if not already present) and update it as you go. - Detect the current phase using the entry signals above. If a checklist with partial progress is already present, ask the user whether to resume.
- Read only the frontmatter of all phase files to confirm which one applies — do not load the full recipes yet:
# bash / zsh bash scripts/read_phases.sh
The frontmatter# PowerShell pwsh scripts/read_phases.ps1entry,exit, anddescriptionfields are sufficient to identify the applicable phase without loading full recipes into context. - Once the applicable phase is confirmed, load and follow its full recipe (
assets/phases/<N>_<name>.md).
References
The user may place relevant papers, code snippets, or other resources in the references folder — check them only when prompted to do so within a particular phase, and refer back / review with the user as needed.
Optional tips
TIPS.md collects optional, use-case-dependent practices (metric uncertainty, optimizer-noise controls, structured result logging, trivial baselines) kept out of the core recipes because their relevance varies by domain. Consult it — or fold relevant items into your fork — as needed.