predict

name	predict
description	Make predictions using a trained AutoMol model. Auto-discovers models from the registry. Use when the user wants to run inference on new molecules.
allowed-tools	Read, Glob, Grep, Bash, AskUserQuestion

AutoMol — Predict

Single-phase skill: load registry, select model, run predictions, show results.

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Step 1: Load Registry

Read MolagentFiles/model_registry.json.

• If the file does not exist or is empty → tell the user: "No trained models found. Use the train-pipeline skill to train a model first." STOP.

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Step 2: Select Model

0 models: STOP (handled above).

1 model: Auto-select it. Display a brief summary:

Using model: {id}
  Target: {target_properties}
  Task: {task_type}
  Metrics: {formatted_metrics or "No metrics available"}
  Model file: {model_file}

N models: Present choices via AskUserQuestion:

AskUserQuestion:
  question: "Which model do you want to use for predictions?"
  header: "Model"
  options: (up to 4 models, most recent first)
    - "{id} — {target_properties} ({task_type}, {key_metric})"
    - ...

After selection, display the model summary as above.

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Step 3: Get Input

If the user already provided input molecules (CSV path or SMILES strings) in their original message, use that directly.

Otherwise, ask:

AskUserQuestion:
  question: "How would you like to provide molecules for prediction?"
  header: "Input"
  options:
    - "CSV file — I have a CSV with a SMILES column"
    - "SMILES strings — I'll paste individual SMILES"

For CSV file: ask for the path if not provided. Do NOT force --smiles-column from the training config — let predict.py auto-detect the column in the user's new data (it tries SMILES, smiles, standardized_smiles, Stand_SMILES). Only add --smiles-column if the user explicitly specifies a non-standard column name.

For SMILES strings: collect the SMILES. Each becomes a --smiles-list argument.

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Step 4: Check for 3D Feature Warning

Read the registry entry's feature_keys. If it contains prolif or AffGraph:

WARNING: This model uses 3D protein-ligand features ({feature_keys}).
Predictions require corresponding protein structures for each molecule.
If you don't have matching 3D data, predictions may fail or be unreliable.

Ask via AskUserQuestion whether to proceed.

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Step 5: Run Prediction

Build the command from the registry entry. The approach depends on model_format:

Merged model (model_format: "merged", model_file is a single string):

Single invocation predicts all properties at once:

uv run python ${AUTOMOL_ROOT:-$PWD}/skills/predict/scripts/predict.py \
    --model-file {model_file} \
    {--smiles-file INPUT_CSV | --smiles-list "SMILES1" --smiles-list "SMILES2"} \
    --output-folder MolagentFiles/ \
    --verbose

To predict a subset of properties: add --properties prop1 --properties prop2.

Individual models (model_format: "individual", model_file is a list):

One invocation per model file (existing behavior):

uv run python ${AUTOMOL_ROOT:-$PWD}/skills/predict/scripts/predict.py \
    --model-file {model_file_path} \
    {--smiles-file INPUT_CSV | --smiles-list "SMILES1" --smiles-list "SMILES2"} \
    --output-folder MolagentFiles/ \
    --verbose

Add --blender-properties {bp} for each entry in the registry's blender_properties array (only for CSV input — the script reads values from the CSV columns).

For CLI SMILES with blender properties, add --blender-values {prop}={value} — ask user for values if not provided.

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Step 6: Show Results

1. Read the output CSV file (merged: MolagentFiles/predictions.csv, individual: MolagentFiles/{property}_predictions.csv)
2. Display a table of the first 5-10 predictions
3. Report total count and output file path

Predictions complete!

  Model: {id}
  Input: {n} molecules
  Output: {output_path}

Sample predictions:
  {table of first 5 rows}

For API serving, see the model card for deployment instructions.

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Important Notes

• ${AUTOMOL_ROOT:-$PWD} resolves to this plugin's root directory (set by SessionStart hook, falls back to $PWD)
• Python scripts: uv run python ... (no venv activation needed — uv run handles dependencies)
• The predict script auto-detects SMILES columns — don't force the training column name on new data
• Blender properties are auto-detected from train_info.json if not explicitly provided

Additional Resources

• examples.md — Usage examples