diff --git a/README.md b/README.md
index f0f568a..5fc01eb 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,6 @@ A package for running OELLM CLI workflows across multiple HPC clusters using SLU
 ## Currently supported workflows
 - Schedule evaluations on multiple models and tasks on all clusters ✅ `oellm schedule-eval ...`
 - Restart failed evaluations (e.g., due to node failures) ✅ `oellm collect-results ... --reschedule true`
-- Interactive eval job/csv builder ✅ `oellm build-csv`
-  - Recursively resolve local paths: pass a directory containing models and their nested intermediate checkpoints, will eval all checkpoints
-  - Support default task groups (cf `oellm/resources/task-groups.yaml`)
 
 ## Planned workflows
 - Sync and download evaluation results from all clusters via a shared data layer
@@ -38,23 +35,7 @@ This will automatically:
 
 In case you meet HuggingFace quotas issues, make sure you are logged in by setting your `HF_TOKEN` and that you are part of [OpenEuroLLM](https://huggingface.co/OpenEuroLLM) organization.
 
-## Interactive CSV Builder
-
-```bash
-oellm interactive-csv
-```
-
-This will launch an interactive workflow where you can:
-- Add models (HuggingFace Hub or local paths)
-- Select evaluation tasks
-- Configure n-shot settings
-- Preview and save your evaluation configuration
-
-The resulting CSV includes an additional `eval_suite` column that records which
-evaluation framework (e.g., `lm_eval` or `lighteval`) should be used for each
-task.
-
-Otherwise you can also directly schedule using a CSV file:
+You can also directly schedule using a CSV file:
 ```bash
 oellm schedule-eval --eval_csv_path custom_evals.csv
 ```
diff --git a/oellm/interactive_csv_builder.py b/oellm/interactive_csv_builder.py
deleted file mode 100644
index 61c99f1..0000000
--- a/oellm/interactive_csv_builder.py
+++ /dev/null
@@ -1,400 +0,0 @@
-import signal
-import sys
-from importlib.resources import files
-from pathlib import Path
-
-import pandas as pd
-import questionary
-import yaml
-from questionary import Style
-from rich import box
-from rich.console import Console
-from rich.panel import Panel
-from rich.progress import Progress, SpinnerColumn, TextColumn
-from rich.table import Table
-
-
-def build_csv_interactive(output_path: str = "eval_config.csv") -> None:
-    """
-    Enhanced interactive CSV builder with arrow key navigation.
-
-    Args:
-        output_path: Path where the CSV file will be saved.
-    """
-    console = Console()
-
-    # Set up signal handler for graceful exit
-    def signal_handler(sig, frame):
-        console.print("\n\n[yellow]Interrupted by user. Exiting...[/yellow]")
-        sys.exit(0)
-
-    signal.signal(signal.SIGINT, signal_handler)
-
-    # Custom style for questionary
-    custom_style = Style(
-        [
-            ("qmark", "fg:#673ab7 bold"),
-            ("question", "bold"),
-            ("answer", "fg:#f44336 bold"),
-            ("pointer", "fg:#673ab7 bold"),
-            ("highlighted", "fg:#673ab7 bold"),
-            ("selected", "fg:#cc5454"),
-            ("separator", "fg:#cc5454"),
-            ("instruction", "fg:#abb2bf"),
-            ("text", ""),
-            ("disabled", "fg:#858585 italic"),
-        ]
-    )
-
-    # Clear screen and show header
-    console.clear()
-    console.print(
-        Panel.fit(
-            "[bold cyan]OpenEuroLLM Evaluation Configuration Builder[/bold cyan]\n"
-            "[dim]Use arrow keys to navigate, Enter to select, Ctrl+C to exit[/dim]",
-            border_style="cyan",
-        )
-    )
-
-    # Step 1: Get models with enhanced input
-    console.print("\n[bold cyan]📦 Step 1: Add Models[/bold cyan]")
-
-    models = []
-    add_more = True
-
-    while add_more:
-        try:
-            action = questionary.select(
-                "What would you like to do?",
-                choices=[
-                    "➕ Add a model",
-                    "📋 View current models"
-                    if models
-                    else questionary.Choice(
-                        "📋 View current models", disabled="No models added yet"
-                    ),
-                    "✅ Continue to tasks"
-                    if models
-                    else questionary.Choice(
-                        "✅ Continue to tasks", disabled="Add at least one model first"
-                    ),
-                ],
-                style=custom_style,
-            ).ask()
-
-            if action is None:  # User pressed Ctrl+C
-                console.print("\n[yellow]Cancelled by user.[/yellow]")
-                return
-        except (KeyboardInterrupt, EOFError):
-            console.print("\n[yellow]Cancelled by user.[/yellow]")
-            return
-
-        if action == "➕ Add a model":
-            model = questionary.text(
-                "Enter model (HuggingFace ID or local path):",
-                instruction="(e.g., meta-llama/Llama-2-7b-hf or /path/to/model)",
-                style=custom_style,
-            ).ask()
-
-            if model is None:  # User pressed Ctrl+C
-                console.print("\n[yellow]Cancelled by user.[/yellow]")
-                return
-
-            if model:
-                models.append(model)
-                console.print(f"[green]✓ Added: {model}[/green]")
-
-        elif action == "📋 View current models":
-            console.print("\n[bold]Current models:[/bold]")
-            for i, model in enumerate(models, 1):
-                console.print(f"  {i}. [cyan]{model}[/cyan]")
-            console.print()
-
-        elif action == "✅ Continue to tasks":
-            add_more = False
-
-    # Step 2: Configure tasks
-    console.print("\n[bold cyan]📝 Step 2: Configure Tasks[/bold cyan]")
-
-    task_configs: list[tuple[str, list[int], str]] = []
-    add_more = True
-
-    # Load task groups from packaged resources
-    task_groups = {}
-    try:
-        data = yaml.safe_load((files("oellm.resources") / "task-groups.yaml").read_text())
-        task_groups = data.get("task_groups", {})
-    except Exception as e:
-        console.print(f"[yellow]Warning: Could not load task groups: {e}[/yellow]")
-
-    while add_more:
-        choices = [
-            "➕ Add a single task",
-        ]
-
-        # Add task group options if available
-        if task_groups:
-            choices.insert(0, "📦 Use a default task group")
-
-        choices.extend(
-            [
-                "📋 View current tasks"
-                if task_configs
-                else questionary.Choice(
-                    "📋 View current tasks", disabled="No tasks added yet"
-                ),
-                "✅ Continue to preview"
-                if task_configs
-                else questionary.Choice(
-                    "✅ Continue to preview", disabled="Add at least one task first"
-                ),
-            ]
-        )
-
-        action = questionary.select(
-            "What would you like to do?",
-            choices=choices,
-            style=custom_style,
-        ).ask()
-
-        if action is None:
-            console.print("\n[yellow]Cancelled by user.[/yellow]")
-            return
-
-        if action == "📦 Use a default task group":
-            # Show available task groups
-            group_choices = []
-            for group_name, group_data in task_groups.items():
-                description = group_data.get("description", "")
-                group_choices.append(f"{group_name} - {description}")
-
-            selected_groups = questionary.checkbox(
-                "Select task groups (↑↓ to navigate, SPACE to check/uncheck, ENTER when done):",
-                choices=group_choices,
-                style=custom_style,
-                instruction="Use SPACEBAR to select groups, not typing text",
-            ).ask()
-
-            if selected_groups is None:
-                console.print("\n[yellow]Cancelled by user.[/yellow]")
-                return
-
-            # Only process if groups were actually selected
-            if selected_groups:
-                # Add tasks from selected groups
-                for selection in selected_groups:
-                    group_name = selection.split(" - ")[0]
-                    group_data = task_groups[group_name]
-
-                    console.print(f"\n[cyan]Adding tasks from '{group_name}':[/cyan]")
-                    for task_item in group_data.get("tasks", []):
-                        task_name = task_item["task"]
-                        n_shots = task_item.get("n_shots", [0])
-                        suite = task_item.get("suite", "lm_eval")
-                        task_configs.append((task_name, n_shots, suite))
-                        console.print(
-                            f"  [green]✓ Added: {task_name} (suite={suite}) with n_shot={n_shots}[/green]"
-                        )
-
-                # After adding task groups, ask if user wants to add more or proceed
-                proceed_choice = questionary.select(
-                    "\nTask groups added. What would you like to do?",
-                    choices=[
-                        "✅ Continue to preview",
-                        "➕ Add more tasks",
-                    ],
-                    style=custom_style,
-                ).ask()
-
-                if proceed_choice is None:
-                    console.print("\n[yellow]Cancelled by user.[/yellow]")
-                    return
-
-                if proceed_choice == "✅ Continue to preview":
-                    add_more = False
-                # If user chooses "Add more tasks", the loop continues
-            else:
-                console.print("\n[yellow]No task groups selected.[/yellow]")
-
-        elif action == "➕ Add a single task":
-            # Direct task input
-            task = questionary.text("Enter task name:", style=custom_style).ask()
-            if task is None:
-                console.print("\n[yellow]Cancelled by user.[/yellow]")
-                return
-
-            if task:
-                # Get n_shot values
-                n_shot_choice = questionary.select(
-                    f"Select n_shot configuration for '{task}':",
-                    choices=[
-                        "0 (zero-shot)",
-                        "5 (few-shot)",
-                        "0,5 (both)",
-                        "0,5,10,25 (multiple)",
-                        "📝 Custom values",
-                    ],
-                    style=custom_style,
-                ).ask()
-
-                if n_shot_choice is None:
-                    console.print("\n[yellow]Cancelled by user.[/yellow]")
-                    return
-
-                if n_shot_choice == "📝 Custom values":
-                    n_shots_str = questionary.text(
-                        "Enter n_shot values (comma-separated):",
-                        instruction="(e.g., 0,5,10)",
-                        style=custom_style,
-                    ).ask()
-                    if n_shots_str is None:
-                        console.print("\n[yellow]Cancelled by user.[/yellow]")
-                        return
-                else:
-                    # Extract numbers from the choice
-                    import re
-
-                    n_shots_str = ",".join(re.findall(r"\d+", n_shot_choice))
-
-                try:
-                    n_shots = [int(x.strip()) for x in n_shots_str.split(",")]
-                    suite_choice = questionary.select(
-                        f"Select evaluation suite for '{task}':",
-                        choices=[
-                            questionary.Choice(
-                                "lm_eval (lm-eval-harness)", value="lm_eval"
-                            ),
-                            questionary.Choice(
-                                "lighteval (Hugging Face LightEval)",
-                                value="lighteval",
-                            ),
-                            "📝 Custom suite",
-                        ],
-                        style=custom_style,
-                    ).ask()
-
-                    if suite_choice is None:
-                        console.print("\n[yellow]Cancelled by user.[/yellow]")
-                        return
-
-                    if suite_choice == "📝 Custom suite":
-                        suite = questionary.text(
-                            "Enter suite identifier:",
-                            instruction="(e.g., custom-eval-suite)",
-                            style=custom_style,
-                        ).ask()
-                        if suite is None:
-                            console.print("\n[yellow]Cancelled by user.[/yellow]")
-                            return
-                        suite = suite.strip()
-                        if not suite:
-                            suite = "lm_eval"
-                    else:
-                        suite = suite_choice
-
-                    task_configs.append((task, n_shots, suite))
-                    console.print(
-                        f"[green]✓ Added: {task} (suite={suite}) with n_shot={n_shots}[/green]"
-                    )
-                except ValueError:
-                    console.print("[red]Invalid n_shot values. Skipping.[/red]")
-
-        elif action == "📋 View current tasks":
-            console.print("\n[bold]Current tasks:[/bold]")
-            for i, (task, n_shots, suite) in enumerate(task_configs, 1):
-                console.print(
-                    f"  {i}. [green]{task}[/green] → n_shot={n_shots} (suite={suite})"
-                )
-            console.print()
-
-        elif action == "✅ Continue to preview":
-            add_more = False
-
-    # Build the dataframe
-    with Progress(
-        SpinnerColumn(),
-        TextColumn("[progress.description]{task.description}"),
-        transient=True,
-    ) as progress:
-        task = progress.add_task("Building configuration matrix...", total=None)
-
-        rows = []
-        for model in models:
-            for task_name, n_shots, suite in task_configs:
-                for n_shot in n_shots:
-                    rows.append(
-                        {
-                            "model_path": model,
-                            "task_path": task_name,
-                            "n_shot": n_shot,
-                            "eval_suite": suite,
-                        }
-                    )
-
-        df = pd.DataFrame(rows)
-        progress.update(task, completed=True)
-
-    # Show preview
-    console.print("\n[bold cyan]👁️  Preview[/bold cyan]")
-
-    table = Table(show_header=True, header_style="bold magenta", box=box.ROUNDED)
-    table.add_column("#", style="dim", width=4)
-    table.add_column("Model", style="cyan", no_wrap=True)
-    table.add_column("Task", style="green")
-    table.add_column("n_shot", justify="right", style="yellow")
-    table.add_column("Suite", style="magenta")
-
-    # Show first 10 rows
-    for idx, (_, row) in enumerate(df.head(10).iterrows(), 1):
-        table.add_row(
-            str(idx),
-            str(row["model_path"]),
-            str(row["task_path"]),
-            str(row["n_shot"]),
-            str(row["eval_suite"]),
-        )
-
-    if len(df) > 10:
-        table.add_row("...", "...", "...", "...")
-
-    console.print(table)
-    console.print(f"\n[bold]Total configurations: {len(df)}[/bold]")
-
-    # Summary statistics
-    console.print("\n[bold cyan]📊 Summary[/bold cyan]")
-    console.print(f"  • Models: {len(models)}")
-    console.print(f"  • Tasks: {len(task_configs)}")
-    console.print(f"  • Total evaluations: {len(df)}")
-
-    # Save confirmation
-    save = questionary.confirm(
-        f"\nSave configuration to {output_path}?", default=True, style=custom_style
-    ).ask()
-
-    if save is None:
-        console.print("\n[yellow]Cancelled by user.[/yellow]")
-        return
-
-    if save:
-        # Ensure directory exists
-        output_dir = Path(output_path).parent
-        if output_dir != Path("."):
-            output_dir.mkdir(parents=True, exist_ok=True)
-
-        with Progress(
-            SpinnerColumn(),
-            TextColumn("[progress.description]{task.description}"),
-            transient=True,
-        ) as progress:
-            task = progress.add_task(f"Saving to {output_path}...", total=None)
-            df.to_csv(output_path, index=False)
-            progress.update(task, completed=True)
-
-        console.print(f"\n[green]✅ Configuration saved to {output_path}[/green]")
-        console.print("\n[bold]Next steps:[/bold]")
-        console.print(f"  1. Review the configuration: [cyan]cat {output_path}[/cyan]")
-        console.print(
-            f"  2. Run evaluation: [cyan]oellm schedule-eval --eval_csv_path {output_path}[/cyan]"
-        )
-    else:
-        console.print("\n[yellow]Configuration not saved.[/yellow]")
diff --git a/oellm/main.py b/oellm/main.py
index c1ffac2..c1d7f5d 100644
--- a/oellm/main.py
+++ b/oellm/main.py
@@ -351,25 +351,6 @@ def schedule_evals(
         )
 
 
-def build_csv(
-    output_path: str = "eval_config.csv",
-    *,
-    verbose: bool = False,
-) -> None:
-    """
-    Build a CSV file for evaluation with per-task n_shot configurations using the interactive builder.
-
-    Args:
-        output_path: Path where the CSV file will be saved.
-        verbose: Enable verbose logging.
-    """
-    _setup_logging(verbose)
-
-    from oellm.interactive_csv_builder import build_csv_interactive
-
-    build_csv_interactive(output_path)
-
-
 def collect_results(
     results_dir: str,
     output_csv: str = "eval_results.csv",
@@ -651,7 +632,6 @@ def main():
     auto_cli(
         {
             "schedule-eval": schedule_evals,
-            "build-csv": build_csv,
             "collect-results": collect_results,
             "clean-cache": lambda: clear_task_cache(),
         },
diff --git a/pyproject.toml b/pyproject.toml
index d699cba..42855e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,6 @@ dependencies = [
     "pydantic<2.12",
     "huggingface_hub",
     "pyyaml",
-    "questionary",
 ]
 
 [project.optional-dependencies]