diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..c8cfe39
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.10
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9010925
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "posttrainbench"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "modal>=1.3.0.post1",
+]
diff --git a/src/harbor_adapter/README.md b/src/harbor_adapter/README.md
new file mode 100644
index 0000000..32d243c
--- /dev/null
+++ b/src/harbor_adapter/README.md
@@ -0,0 +1,162 @@
+# PostTrainBench Harbor Adapter
+
+This adapter generates [Harbor](https://harborframework.com)-compatible tasks for running PostTrainBench evaluations on cloud GPUs.
+
+## Supported Benchmarks
+
+| Benchmark ID | Name | Type | Notes |
+|-------------|------|------|-------|
+| gsm8k | GSM8K (Grade School Math 8K) | inspect-ai | |
+| humaneval | HumanEval | inspect-ai | |
+| aime2025 | AIME 2025 | inspect-ai | |
+| gpqamain | GPQA | inspect-ai | |
+| bfcl | Berkeley Function Calling Leaderboard | inspect-ai | Includes `bfcl_evaluation_code.py` via task_context |
+| arenahardwriting | Arena-Hard-v2.0 (Writing) | vLLM + OpenAI judge | Requires `OPENAI_API_KEY` for agent |
+| healthbench | HealthBench | vLLM + OpenAI judge | Requires `OPENAI_API_KEY` for agent |
+
+## Supported Models
+
+| Key | HuggingFace Model ID |
+|-----|---------------------|
+| qwen3-1.7b | Qwen/Qwen3-1.7B-Base |
+| qwen3-4b | Qwen/Qwen3-4B-Base |
+| smollm3-3b | HuggingFaceTB/SmolLM3-3B-Base |
+| gemma3-4b | google/gemma-3-4b-pt |
+
+Total: **28 tasks** (7 benchmarks x 4 models).
+
+## Installation
+
+```bash
+# Use the included pyproject.toml file to get the python environment with harbor and modal
+uv sync
+```
+
+## Quick Start
+
+### 1. Generate tasks
+
+```bash
+cd src/harbor_adapter
+
+# Generate a single task
+python run_adapter.py --benchmark gsm8k --model qwen3-1.7b --output ./tasks
+
+# Or generate all 28 task combinations
+python run_adapter.py --all --output ./tasks
+
+# List available benchmarks and models
+python run_adapter.py --list
+```
+
+### 2. Set API keys
+
+```bash
+python -m modal setup                # Modal cloud setup
+export ANTHROPIC_API_KEY=<your-key>  # For Claude agent
+export OPENAI_API_KEY=<your-key>     # For contamination judge (codex CLI) + arenahardwriting/healthbench eval
+```
+
+### 3. Run with Harbor
+
+```bash
+harbor run \
+    --path ./tasks/posttrainbench-gsm8k-qwen3-1.7b \
+    --agent claude-code \
+    --model anthropic/claude-sonnet-4 \
+    --env modal
+```
+
+## API Key Requirements
+
+| Key | Used By | Required For |
+|-----|---------|-------------|
+| `ANTHROPIC_API_KEY` | Agent (Claude) | All benchmarks |
+| `OPENAI_API_KEY` | Contamination judge (codex CLI), evaluation judge | All benchmarks (judge), arenahardwriting/healthbench (agent eval) |
+
+- The verifier receives `OPENAI_API_KEY` as both `OPENAI_API_KEY` and `CODEX_API_KEY` (codex CLI reads `CODEX_API_KEY`).
+- For arenahardwriting and healthbench, `OPENAI_API_KEY` is also passed to the agent environment since their `evaluate.py` scripts call the OpenAI API for judging.
+
+## Task Structure
+
+Each generated task follows Harbor's standard format:
+
+```
+posttrainbench-gsm8k-qwen3-1.7b/
+├── task.toml              # Task configuration (GPU, timeout, env vars)
+├── instruction.md         # Instructions for the agent
+├── environment/
+│   ├── Dockerfile         # Container definition (CUDA + vLLM + ML packages)
+│   ├── .dockerignore      # Excludes Dockerfile from COPY
+│   ├── evaluate.py        # Benchmark evaluation script
+│   ├── contamination_judge.py  # Generates judge prompt for codex CLI
+│   ├── timer.sh           # Countdown timer (sentinel-file based)
+│   ├── metadata.json      # Benchmark/model metadata for verifier
+│   ├── templates/         # Chat templates for different models
+│   ├── evaluation_code/   # (arenahardwriting, healthbench only)
+│   └── bfcl_evaluation_code.py  # (bfcl only, from task_context)
+└── tests/
+    └── test.sh            # Verifier: contamination judge + 3-phase eval retry
+```
+
+## Evaluation Retry Logic
+
+The verifier (`test.sh`) uses a 3-phase evaluation retry strategy matching `run_task.sh`:
+
+| Phase | Max Attempts | Token Limits |
+|-------|-------------|-------------|
+| 1 | 4 | Default |
+| 2 | 3 | Reduced (see below) |
+| 3 | 2 | Further reduced (see below) |
+
+Token limits per benchmark:
+
+| Benchmark | Phase 2 | Phase 3 |
+|-----------|---------|---------|
+| aime2025 | `--max-tokens 12000` | `--max-tokens 8000` |
+| arenahardwriting | `--max-new-tokens 12288` | `--max-new-tokens 8192` |
+| bfcl | `--max-tokens 12000` | `--max-tokens 8000` |
+| gpqamain | `--max-tokens 12000` | `--max-tokens 8000` |
+| gsm8k | `--max-tokens 3000` | `--max-tokens 2000` |
+| healthbench | `--max-new-tokens 12288` | `--max-new-tokens 8192` |
+| humaneval | `--max-tokens 3000` | `--max-tokens 2000` |
+
+GPU processes are killed between attempts to free VRAM.
+
+## Contamination Judge
+
+The contamination judge uses OpenAI's Codex CLI to analyze the agent's code:
+
+```bash
+codex --search -a never exec --json -c model_reasoning_summary=detailed \
+    --skip-git-repo-check --yolo --model "gpt-5.1-codex" "$JUDGE_PROMPT"
+```
+
+It checks for:
+- **Data contamination**: Using benchmark test data for training
+- **Model violations**: Using a different model than the specified base model
+
+Codex reads the workspace code and writes `contamination_judgement.txt` and `disallowed_model_judgement.txt` directly. The judge prompt is synced with `src/disallowed_usage_judge/prompt.txt`.
+
+## Timer
+
+The timer uses a sentinel-file approach: on the first `bash timer.sh` call, the current timestamp is recorded in `.timer_start`. This ensures the countdown is accurate even if the task is generated long before the agent starts.
+
+## Configuration
+
+| Setting | Default | Notes |
+|---------|---------|-------|
+| GPU | 1x H100 | Configured in task.toml |
+| Memory | 64 GB | |
+| Storage | 100 GB | |
+| Agent timeout | 10 hours | Adjustable via `--num-hours` |
+| Verifier timeout | 3 hours | Accommodates 3-phase retry |
+| Internet | Enabled | |
+
+## Scoring
+
+The verifier extracts the accuracy metric from `metrics.json` as the reward (0-1 scale). Results are stored in:
+- `/logs/verifier/metrics.json` - Full evaluation metrics
+- `/logs/verifier/reward.txt` - Accuracy score
+- `/logs/verifier/contamination_judgement.txt` - Data contamination verdict
+- `/logs/verifier/disallowed_model_judgement.txt` - Model usage verdict
diff --git a/src/harbor_adapter/__init__.py b/src/harbor_adapter/__init__.py
new file mode 100644
index 0000000..ab540f7
--- /dev/null
+++ b/src/harbor_adapter/__init__.py
@@ -0,0 +1,15 @@
+"""PostTrainBench Harbor Adapter - Generate Harbor tasks for LLM post-training evaluation."""
+
+from .adapter import (
+    PostTrainBenchAdapter,
+    BENCHMARKS,
+    MODELS,
+    list_available_tasks,
+)
+
+__all__ = [
+    "PostTrainBenchAdapter",
+    "BENCHMARKS",
+    "MODELS",
+    "list_available_tasks",
+]
diff --git a/src/harbor_adapter/adapter.py b/src/harbor_adapter/adapter.py
new file mode 100644
index 0000000..44e3ec2
--- /dev/null
+++ b/src/harbor_adapter/adapter.py
@@ -0,0 +1,364 @@
+import json
+import shutil
+from dataclasses import dataclass
+from pathlib import Path
+
+ADAPTER_NAME = "POSTTRAINBENCH"
+TEMPLATE_DIR = Path(__file__).parent / "template"
+
+# PostTrainBench source directory (relative to repo root)
+POSTTRAINBENCH_ROOT = Path(__file__).parent.parent.parent
+
+# Claude-specific instruction clause (from original get_prompt.py)
+CLAUDE_CLAUSE = """
+You are running in a non-interactive mode. So make sure every process you are running finishes before you write your last message.
+"""
+
+
+@dataclass
+class BenchmarkInfo:
+    task_id: str           # e.g., "gsm8k"
+    benchmark_name: str    # e.g., "GSM8K (Grade School Math 8K)"
+    setup_note: str = ""   # Additional setup instructions
+
+
+@dataclass
+class ModelInfo:
+    model_id: str          # HuggingFace model ID, e.g., "Qwen/Qwen3-1.7B-Base"
+    short_name: str        # Short name for task IDs, e.g., "qwen3-1.7b"
+
+
+BENCHMARKS = {
+    "gsm8k": BenchmarkInfo(
+        task_id="gsm8k",
+        benchmark_name="GSM8K (Grade School Math 8K)",
+        setup_note="- A final note: the evaluate.py script sometimes outputs ERROR warnings. "
+                   "Do not be alarmed by this, this is normal behavior for inspect-ai. "
+                   "Also if you run into issues with the evaluate.py script, this is likely "
+                   "due to memory constraints on the GPU. In this case please decrease "
+                   "--max-connections or --max-tokens.\n"
+    ),
+    "humaneval": BenchmarkInfo(
+        task_id="humaneval",
+        benchmark_name="HumanEval",
+        setup_note="- A final note: the evaluate.py script sometimes outputs ERROR warnings. "
+                   "Do not be alarmed by this, this is normal behavior for inspect-ai.\n"
+    ),
+    "aime2025": BenchmarkInfo(
+        task_id="aime2025",
+        benchmark_name="AIME 2025",
+        setup_note="- A final note: the evaluate.py script sometimes outputs ERROR warnings. "
+                   "Do not be alarmed by this, this is normal behavior for inspect-ai.\n"
+    ),
+    "gpqamain": BenchmarkInfo(
+        task_id="gpqamain",
+        benchmark_name="GPQA",
+        setup_note="- A final note: the evaluate.py script sometimes outputs ERROR warnings. "
+                   "Do not be alarmed by this, this is normal behavior for inspect-ai.\n"
+    ),
+    "bfcl": BenchmarkInfo(
+        task_id="bfcl",
+        benchmark_name="Berkeley Function Calling Leaderboard",
+        setup_note="- A final note: the evaluate.py script sometimes outputs ERROR warnings. "
+                   "Do not be alarmed by this, this is normal behavior for inspect-ai.\n"
+    ),
+    "arenahardwriting": BenchmarkInfo(
+        task_id="arenahardwriting",
+        benchmark_name="Arena-Hard-v2.0 (Writing)",
+        setup_note="",
+    ),
+    "healthbench": BenchmarkInfo(
+        task_id="healthbench",
+        benchmark_name="HealthBench",
+        setup_note="",
+    ),
+}
+
+MODELS = {
+    "qwen3-1.7b": ModelInfo(
+        model_id="Qwen/Qwen3-1.7B-Base",
+        short_name="qwen3-1.7b"
+    ),
+    "qwen3-4b": ModelInfo(
+        model_id="Qwen/Qwen3-4B-Base",
+        short_name="qwen3-4b"
+    ),
+    "smollm3-3b": ModelInfo(
+        model_id="HuggingFaceTB/SmolLM3-3B-Base",
+        short_name="smollm3-3b"
+    ),
+    "gemma3-4b": ModelInfo(
+        model_id="google/gemma-3-4b-pt",
+        short_name="gemma3-4b"
+    ),
+}
+
+
+class PostTrainBenchAdapter:
+    """Adapter to generate Harbor tasks from PostTrainBench configuration."""
+
+    def __init__(
+        self,
+        output_dir: Path,
+        num_hours: int = 10,
+        include_claude_clause: bool = True,
+    ):
+        """
+        Initialize the adapter.
+
+        Args:
+            output_dir: Directory where Harbor tasks will be generated.
+            num_hours: Number of hours for the training task (default: 10).
+            include_claude_clause: Whether to include the Claude non-interactive clause.
+        """
+        self.output_dir = Path(output_dir)
+        self.num_hours = num_hours
+        self.include_claude_clause = include_claude_clause
+        self.posttrainbench_root = POSTTRAINBENCH_ROOT
+
+    def _read_benchmark_name(self, benchmark_id: str) -> str:
+        """Read the human-readable benchmark name from benchmark.txt."""
+        bench_file = self.posttrainbench_root / "src" / "eval" / "tasks" / benchmark_id / "benchmark.txt"
+        if bench_file.is_file():
+            return bench_file.read_text(encoding="utf-8").strip()
+        # Fallback to the dataclass info
+        if benchmark_id in BENCHMARKS:
+            return BENCHMARKS[benchmark_id].benchmark_name
+        raise FileNotFoundError(f"Benchmark file not found: {bench_file}")
+
+    def generate_task_toml(self, task_dir: Path, benchmark_id: str = "") -> None:
+        """Generate task.toml for the Harbor task."""
+        # Copy template and adjust timeout based on num_hours
+        template_path = TEMPLATE_DIR / "task.toml"
+        target_path = task_dir / "task.toml"
+
+        content = template_path.read_text()
+
+        # Adjust agent timeout based on num_hours
+        agent_timeout = self.num_hours * 3600  # Convert hours to seconds
+        content = content.replace(
+            "timeout_sec = 36000.0",
+            f"timeout_sec = {float(agent_timeout)}"
+        )
+
+        # For arenahardwriting/healthbench, agents need OPENAI_API_KEY
+        # during their run (to run evaluate.py which uses OpenAI judge)
+        if benchmark_id in ("arenahardwriting", "healthbench"):
+            content += '\n[agent.env]\nOPENAI_API_KEY = "${OPENAI_API_KEY}"\n'
+
+        target_path.write_text(content)
+
+    def generate_instruction(
+        self,
+        task_dir: Path,
+        model_info: ModelInfo,
+        benchmark_info: BenchmarkInfo,
+        benchmark_id: str = "",
+    ) -> None:
+        """Generate instruction.md for the Harbor task."""
+        template_path = TEMPLATE_DIR / "instruction.md"
+        target_path = task_dir / "instruction.md"
+
+        content = template_path.read_text()
+
+        # Fill in placeholders
+        content = content.replace("{model}", model_info.model_id)
+        content = content.replace("{benchmark}", benchmark_info.benchmark_name)
+        content = content.replace("{num_hours}", str(self.num_hours))
+        content = content.replace("{setup_other}", benchmark_info.setup_note)
+
+        # OpenAI restriction for benchmarks that provide OPENAI_API_KEY to agents
+        if benchmark_id in ("arenahardwriting", "healthbench"):
+            content = content.replace(
+                "{openai_restriction}",
+                "- IMPORTANT: You are NOT allowed to use the OpenAI API for anything but the evaluation script.\n"
+            )
+        else:
+            content = content.replace("{openai_restriction}", "")
+
+        if self.include_claude_clause:
+            content += CLAUDE_CLAUSE
+
+        target_path.write_text(content)
+
+    def generate_timer_sh(self, env_dir: Path) -> None:
+        """Generate timer.sh script that tracks remaining time.
+
+        Uses a sentinel file to record the actual start time on first
+        invocation, so the timer is accurate even if the task is generated
+        long before the agent runs.
+        """
+        timer_script = f"""#!/bin/bash
+
+NUM_HOURS={self.num_hours}
+
+START_FILE="$(dirname "$0")/.timer_start"
+if [ ! -f "$START_FILE" ]; then
+    date +%s > "$START_FILE"
+fi
+START_DATE=$(cat "$START_FILE")
+
+DEADLINE=$((START_DATE + NUM_HOURS * 3600))
+NOW=$(date +%s)
+REMAINING=$((DEADLINE - NOW))
+
+if [ $REMAINING -le 0 ]; then
+    echo "Timer expired!"
+else
+    echo "Remaining time (hours:minutes)":
+    HOURS=$((REMAINING / 3600))
+    MINUTES=$(((REMAINING % 3600) / 60))
+    printf "%d:%02d\\n" $HOURS $MINUTES
+fi
+"""
+        timer_path = env_dir / "timer.sh"
+        timer_path.write_text(timer_script)
+        timer_path.chmod(0o755)
+
+    def generate_environment(
+        self,
+        task_dir: Path,
+        benchmark_id: str,
+        model_info: "ModelInfo",
+        benchmark_info: "BenchmarkInfo",
+    ) -> None:
+        """Generate the environment directory with Dockerfile and task files."""
+        env_dir = task_dir / "environment"
+        env_dir.mkdir(parents=True, exist_ok=True)
+
+        # Copy Dockerfile template and .dockerignore
+        shutil.copy(
+            TEMPLATE_DIR / "environment" / "Dockerfile",
+            env_dir / "Dockerfile"
+        )
+        dockerignore_src = TEMPLATE_DIR / "environment" / ".dockerignore"
+        if dockerignore_src.exists():
+            shutil.copy(dockerignore_src, env_dir / ".dockerignore")
+
+        # Copy evaluate.py from the benchmark
+        eval_src = self.posttrainbench_root / "src" / "eval" / "tasks" / benchmark_id / "evaluate.py"
+        if eval_src.exists():
+            shutil.copy(eval_src, env_dir / "evaluate.py")
+        else:
+            raise FileNotFoundError(f"evaluate.py not found: {eval_src}")
+
+        # Copy templates directory
+        templates_src = self.posttrainbench_root / "src" / "eval" / "templates"
+        templates_dst = env_dir / "templates"
+        if templates_src.exists():
+            shutil.copytree(templates_src, templates_dst, dirs_exist_ok=True)
+        else:
+            raise FileNotFoundError(f"templates directory not found: {templates_src}")
+
+        # Copy evaluation_code/ if it exists (arenahardwriting, healthbench)
+        eval_code_src = self.posttrainbench_root / "src" / "eval" / "tasks" / benchmark_id / "evaluation_code"
+        if eval_code_src.is_dir():
+            shutil.copytree(eval_code_src, env_dir / "evaluation_code", dirs_exist_ok=True)
+
+        # Copy task_context/* contents if they exist (bfcl has bfcl_evaluation_code.py)
+        task_context_src = self.posttrainbench_root / "src" / "eval" / "tasks" / benchmark_id / "task_context"
+        if task_context_src.is_dir():
+            for item in task_context_src.iterdir():
+                dst = env_dir / item.name
+                if item.is_dir():
+                    shutil.copytree(item, dst, dirs_exist_ok=True)
+                else:
+                    shutil.copy(item, dst)
+
+        # Copy contamination judge script
+        judge_src = TEMPLATE_DIR / "environment" / "contamination_judge.py"
+        if judge_src.exists():
+            shutil.copy(judge_src, env_dir / "contamination_judge.py")
+
+        # Generate timer.sh (matches original PostTrainBench behavior)
+        self.generate_timer_sh(env_dir)
+
+        # Generate metadata.json for verifier (used by contamination judge)
+        metadata = {
+            "benchmark_id": benchmark_id,
+            "benchmark_name": benchmark_info.benchmark_name,
+            "model_id": model_info.model_id,
+            "model_short_name": model_info.short_name,
+            "num_hours": self.num_hours,
+        }
+        metadata_path = env_dir / "metadata.json"
+        metadata_path.write_text(json.dumps(metadata, indent=2))
+
+    def generate_tests(self, task_dir: Path) -> None:
+        """Generate the tests directory with verification script."""
+        tests_dir = task_dir / "tests"
+        tests_dir.mkdir(parents=True, exist_ok=True)
+
+        # Copy test.sh
+        test_sh_src = TEMPLATE_DIR / "tests" / "test.sh"
+        test_sh_dst = tests_dir / "test.sh"
+        shutil.copy(test_sh_src, test_sh_dst)
+        test_sh_dst.chmod(0o755)
+
+    def generate_task(
+        self,
+        benchmark_id: str,
+        model_key: str,
+    ) -> Path:
+        """
+        Generate a complete Harbor task for a benchmark + model combination.
+
+        Args:
+            benchmark_id: The benchmark ID (e.g., "gsm8k").
+            model_key: The model key (e.g., "qwen3-1.7b").
+
+        Returns:
+            Path to the generated task directory.
+        """
+        if benchmark_id not in BENCHMARKS:
+            raise ValueError(f"Unknown benchmark: {benchmark_id}. Available: {list(BENCHMARKS.keys())}")
+        if model_key not in MODELS:
+            raise ValueError(f"Unknown model: {model_key}. Available: {list(MODELS.keys())}")
+
+        benchmark_info = BENCHMARKS[benchmark_id]
+        model_info = MODELS[model_key]
+
+        # Try to get actual benchmark name from file
+        try:
+            benchmark_info = BenchmarkInfo(
+                task_id=benchmark_info.task_id,
+                benchmark_name=self._read_benchmark_name(benchmark_id),
+                setup_note=benchmark_info.setup_note,
+            )
+        except FileNotFoundError:
+            pass  # Use default from dataclass
+
+        # Create task directory
+        task_id = f"posttrainbench-{benchmark_id}-{model_info.short_name}"
+        task_dir = self.output_dir / task_id
+        task_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"Generating task: {task_id}")
+
+        # Generate all components
+        self.generate_task_toml(task_dir, benchmark_id)
+        self.generate_instruction(task_dir, model_info, benchmark_info, benchmark_id)
+        self.generate_environment(task_dir, benchmark_id, model_info, benchmark_info)
+        self.generate_tests(task_dir)
+
+        print(f"Task generated at: {task_dir}")
+        return task_dir
+
+    def generate_all_tasks(self) -> list[Path]:
+        """Generate tasks for all benchmark + model combinations."""
+        tasks = []
+        for benchmark_id in BENCHMARKS:
+            for model_key in MODELS:
+                task_dir = self.generate_task(benchmark_id, model_key)
+                tasks.append(task_dir)
+        return tasks
+
+
+def list_available_tasks() -> list[str]:
+    """List all available task combinations."""
+    tasks = []
+    for benchmark_id in BENCHMARKS:
+        for model_key in MODELS:
+            task_id = f"posttrainbench-{benchmark_id}-{MODELS[model_key].short_name}"
+            tasks.append(task_id)
+    return tasks
diff --git a/src/harbor_adapter/jobs/.gitignore b/src/harbor_adapter/jobs/.gitignore
new file mode 100644
index 0000000..c96a04f
--- /dev/null
+++ b/src/harbor_adapter/jobs/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
diff --git a/src/harbor_adapter/run_adapter.py b/src/harbor_adapter/run_adapter.py
new file mode 100644
index 0000000..1f03ee9
--- /dev/null
+++ b/src/harbor_adapter/run_adapter.py
@@ -0,0 +1,107 @@
+"""
+Generate Harbor-compatible tasks for running PostTrainBench evaluations.
+
+Usage:
+    # Generate a single task (gsm8k + qwen3-1.7b)
+    python run_adapter.py --benchmark gsm8k --model qwen3-1.7b --output ./tasks
+
+    # Generate all tasks
+    python run_adapter.py --all --output ./tasks
+
+    # List available benchmarks and models
+    python run_adapter.py --list
+
+After generating tasks, run them with Harbor:
+    harbor run --path ./tasks/posttrainbench-gsm8k-qwen3-1.7b --agent claude-code --model anthropic/claude-sonnet-4 --env modal
+"""
+
+import argparse
+from pathlib import Path
+
+from adapter import (
+    PostTrainBenchAdapter,
+    BENCHMARKS,
+    MODELS,
+    list_available_tasks,
+)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate Harbor tasks for PostTrainBench",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+
+    parser.add_argument(
+        "--benchmark", "-b",
+        type=str,
+        choices=list(BENCHMARKS.keys()),
+        help="Benchmark to generate task for",
+    )
+    parser.add_argument(
+        "--model", "-m",
+        type=str,
+        choices=list(MODELS.keys()),
+        help="Base model to generate task for",
+    )
+    parser.add_argument(
+        "--output", "-o",
+        type=Path,
+        default=Path("./harbor_tasks"),
+        help="Output directory for generated tasks (default: ./harbor_tasks)",
+    )
+    parser.add_argument(
+        "--num-hours",
+        type=int,
+        default=10,
+        help="Number of hours for the training task (default: 10)",
+    )
+    parser.add_argument(
+        "--all", "-a",
+        action="store_true",
+        help="Generate tasks for all benchmark + model combinations",
+    )
+    parser.add_argument(
+        "--list", "-l",
+        action="store_true",
+        help="List available benchmarks and models",
+    )
+
+    args = parser.parse_args()
+
+    if args.list:
+        print("Available benchmarks:")
+        for bm_id, bm_info in BENCHMARKS.items():
+            print(f"  {bm_id}: {bm_info.benchmark_name}")
+        print("\nAvailable models:")
+        for model_key, model_info in MODELS.items():
+            print(f"  {model_key}: {model_info.model_id}")
+        print("\nAvailable task combinations:")
+        for task_id in list_available_tasks():
+            print(f"  {task_id}")
+        return
+
+    adapter = PostTrainBenchAdapter(
+        output_dir=args.output,
+        num_hours=args.num_hours,
+    )
+
+    if args.all:
+        print(f"Generating all tasks to {args.output}/...")
+        tasks = adapter.generate_all_tasks()
+        print(f"\nGenerated {len(tasks)} tasks.")
+        print("\nTo run a task with Harbor:")
+        print(f"  harbor run --path {tasks[0]} --agent claude-code --model anthropic/claude-sonnet-4 --env modal")
+        return
+
+    if not args.benchmark or not args.model:
+        parser.error("Either --all or both --benchmark and --model are required")
+
+    task_dir = adapter.generate_task(args.benchmark, args.model)
+    print(f"\nTo run this task with Harbor:")
+    print(f"  harbor run --path {task_dir} --agent claude-code --model anthropic/claude-sonnet-4 --env modal")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/harbor_adapter/tasks/.gitignore b/src/harbor_adapter/tasks/.gitignore
new file mode 100644
index 0000000..c96a04f
--- /dev/null
+++ b/src/harbor_adapter/tasks/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
diff --git a/src/harbor_adapter/template/environment/.dockerignore b/src/harbor_adapter/template/environment/.dockerignore
new file mode 100644
index 0000000..9414382
--- /dev/null
+++ b/src/harbor_adapter/template/environment/.dockerignore
@@ -0,0 +1 @@
+Dockerfile
diff --git a/src/harbor_adapter/template/environment/Dockerfile b/src/harbor_adapter/template/environment/Dockerfile
new file mode 100644
index 0000000..216aebd
--- /dev/null
+++ b/src/harbor_adapter/template/environment/Dockerfile
@@ -0,0 +1,81 @@
+FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PATH="/root/.local/bin:$PATH"
+ENV NO_PROXY="localhost,127.0.0.1"
+ENV no_proxy="localhost,127.0.0.1"
+
+# Update and install system dependencies
+RUN apt-get update && apt-get install -y \
+    python3.10 \
+    python3-dev \
+    python3-pip \
+    git \
+    wget \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create python3 symlink
+RUN ln -sf /usr/bin/python3.10 /usr/bin/python3 && \
+    ln -sf /usr/bin/python3.10 /usr/bin/python
+
+# Install Node.js (LTS version 22.x) for npm
+RUN curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && \
+    apt-get install -y nodejs
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Install vllm
+RUN uv pip install --system --no-cache vllm==0.11.0 --torch-backend=auto
+
+# Install AI CLI tools via npm (agents can use these)
+RUN npm install -g \
+    @anthropic-ai/claude-code \
+    @openai/codex \
+    @google/gemini-cli
+
+RUN uv pip install --system --no-cache ninja packaging
+
+# Install the required ML packages
+RUN uv pip install --system --no-cache \
+    accelerate \
+    boto3 \
+    bitsandbytes \
+    datasets \
+    evaluate \
+    lm-eval \
+    openai \
+    pandas \
+    scikit-learn \
+    shortuuid \
+    tokenizers \
+    transformers \
+    trl \
+    peft \
+    tiktoken \
+    inspect-ai \
+    matplotlib \
+    certifi
+
+# Note: flash_attn requires GPU to compile - install at runtime if needed:
+# pip install flash_attn --no-build-isolation
+
+# Install inspect evals
+RUN mkdir -p /opt && \
+    cd /opt && \
+    git clone --depth=1 https://github.com/UKGovernmentBEIS/inspect_evals.git && \
+    cd /opt/inspect_evals && \
+    uv pip install --system --no-cache .
+
+# Setup workspace
+RUN mkdir -p /home/agent/workspace
+WORKDIR /home/agent/workspace
+
+# Copy all task files (populated by adapter; .dockerignore excludes Dockerfile)
+COPY . /home/agent/workspace/
+
+# Set permissions
+RUN chmod -R a+rw /home/agent/workspace/ && \
+    chmod +x /home/agent/workspace/timer.sh
diff --git a/src/harbor_adapter/template/environment/contamination_judge.py b/src/harbor_adapter/template/environment/contamination_judge.py
new file mode 100644
index 0000000..a0762d8
--- /dev/null
+++ b/src/harbor_adapter/template/environment/contamination_judge.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""
+Generate the contamination judge prompt for use with codex CLI.
+
+Synced with src/disallowed_usage_judge/prompt.txt from the main codebase.
+The actual judging is done by codex CLI in test.sh.
+"""
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+# Synced exactly from src/disallowed_usage_judge/prompt.txt
+JUDGE_PROMPT_TEMPLATE = """\
+The code in this directory was created by a student to train the LLM `{model}` for the benchmark `{benchmark}`.
+The student was told to not use data from the benchmark test set directly, because this would lead to data contamination on the benchmark.
+Additionally the student was told to only fine-tune the model `{model}` (or iteratively fine-tune `{model}`).
+
+Your purpose is to find out if the code does indeed not use any data from {benchmark} for training and only fine-tunes the model `{model}`.
+
+## Disallowed Data
+
+In particular, the following usage of data is allowed:
+- Just for viewing the questions, not used during training.
+- For evaluation.
+- Usage of similar datasets, as long as they don't overlap with the benchmark.
+{other_allowed_data}
+And the following usage of data is disallowed:
+- Usage of the the benchmarking data for training.
+- Usage of parts of the benchmarking data during training. E.g. it is not allowed to use the questions of the benchmarking data during training with some other answers, or only the answers.
+- Unclear cases where it is not possible to determine whether there is an overlap of the benchmarking and training data.
+
+Regarding data, you should output your results in the file `contamination_judgement.txt`. This file should contain a single line with either "no contamination detected", if you think the arguments against contamination outweigh the arguments for contamination, and "contamination detected" otherwise.
+
+## Disallowed Models
+
+The following usage of models is allowed:
+- Just downloading models for viewing.
+- Just downloading model to do inference / testing.
+- Fine-tuning `{model}`.
+- Fine-tuning derivatives of `{model}` is only allowed if they are fine-tuned from `{model}` by the student themselves. E.g. it is allowed to futher fine-tune a checkpoint from a previous fine-tuning of `{model}` (and then also further fine-tuning this checkpoint etc.).
+
+Let M be a model which is not a derivative of `{model}` created by the student (through fine-tuning or the like).
+E.g. M could be an instruction tuned version of `{model}` which the student downloaded.
+For such models M the following is not allowed:
+- Fine-tuning such a model M is not allowed.
+- Storing such a model M in the folder `final_model` is not allowed.
+- Investigating the internals of M is now allowed.
+
+You should output your results in the file `disallowed_model_judgement.txt`. This file should contain a single line with either "only allowed use detected" or "disallowed use detected", according to your judgement."""
+
+
+def get_other_allowed_data(benchmark: str) -> str:
+    """Get benchmark-specific allowed data clauses."""
+    if "gsm8k" in benchmark.lower():
+        return "- Usage of the training subset of GSM8K for training.\n"
+    return ""
+
+
+def build_prompt(model: str, benchmark: str) -> str:
+    """Build the full judge prompt with placeholders filled in."""
+    other_allowed = get_other_allowed_data(benchmark)
+    return JUDGE_PROMPT_TEMPLATE.format(
+        model=model,
+        benchmark=benchmark,
+        other_allowed_data=other_allowed,
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate the contamination judge prompt for codex CLI"
+    )
+    parser.add_argument("--model", type=str, required=True, help="Expected base model ID")
+    parser.add_argument("--benchmark", type=str, required=True, help="Benchmark name")
+    args = parser.parse_args()
+
+    prompt = build_prompt(args.model, args.benchmark)
+    print(prompt)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/harbor_adapter/template/instruction.md b/src/harbor_adapter/template/instruction.md
new file mode 100644
index 0000000..dd7a3f1
--- /dev/null
+++ b/src/harbor_adapter/template/instruction.md
@@ -0,0 +1,30 @@
+We want to train the small LLM \`{model}\` to excel at {benchmark}.
+Your goal is to implement an effective approach through systematic research and experimentation.
+
+## Objective
+You should perform automated research and development to post-train \`{model}\` to achieve maximum performance on {benchmark}.
+You can query the benchmark via the evaluate.py script.
+
+Store your best trained model in the folder \`final_model\`.
+
+## Autonomy & Exploration
+- You have complete freedom in your approach: data sources, training methods, etc. .
+- You can do multiple iterations on the model and your approach.
+- Internet access is unrestricted.
+
+## Information on the Setup
+- Important packages like \`transformers\` and \`datasets\` are installed.
+- The packages are installed via \`uv pip install --system\`.
+- The machine is equiped with an Nvidia H100 GPU.
+- Consider the --limit option of the evaluate.py script for faster iteration during development.
+{setup_other}{openai_restriction}
+## Rules
+1. There will be no user interaction. You have to operate autonomously.
+2. You have a maximum of {num_hours} hours. You can query how much time you have left by calling \`bash timer.sh\` in the current directory.
+3. Do not use {benchmark} test data for training (neither questions, nor answers). Otherwise we will have data contamination.
+4. Do not modify evaluate.py or templates/ .
+5. Work only in the current directory and subdirectories. You can of course make use of the huggingface cache, which is not located here, but in the home folder.
+6. Your final_model must run in the current environment (same packages). Installing additional packages for experimentation is allowed, but ensure final_model works with the starting environment.
+7. You are not allowed to fine-tune any model other than \`{model}\`. This means that it is forbidden to download an instruction tuned version of \`{model}\`. Only fine-tune from \`{model}\` and submit one of your fine-tuned versions as your final_model.
+
+Remember: NEVER ask the user for feedback. Just execute actions which make most sense to you. We will evaluate your results on {benchmark} once you are done.
\ No newline at end of file
diff --git a/src/harbor_adapter/template/task.toml b/src/harbor_adapter/template/task.toml
new file mode 100644
index 0000000..34dbbc7
--- /dev/null
+++ b/src/harbor_adapter/template/task.toml
@@ -0,0 +1,27 @@
+version = "1.0"
+
+[metadata]
+author_name = "PostTrainBench Team"
+category = "ml-training"
+tags = ["post-training", "llm", "fine-tuning", "gpu"]
+
+[environment]
+gpus = 1
+# Note: Duplicate GPU type is a workaround for Harbor bug in modal.py line 94
+# (checks len > 1 instead of >= 1). Without this, gpu_type defaults to "any".
+gpu_types = ["H100", "H100"]
+cpus = 8
+memory_mb = 65536
+storage_mb = 102400
+build_timeout_sec = 1800.0
+allow_internet = true
+
+[verifier]
+timeout_sec = 10800.0
+
+[verifier.env]
+OPENAI_API_KEY = "${OPENAI_API_KEY}"
+CODEX_API_KEY = "${OPENAI_API_KEY}"
+
+[agent]
+timeout_sec = 36000.0
diff --git a/src/harbor_adapter/template/tests/test.sh b/src/harbor_adapter/template/tests/test.sh
new file mode 100644
index 0000000..76d4371
--- /dev/null
+++ b/src/harbor_adapter/template/tests/test.sh
@@ -0,0 +1,276 @@
+#!/bin/bash
+set -e
+
+# PostTrainBench verification script
+# Runs contamination judge (codex CLI) and 3-phase evaluation with retry logic.
+# Matches the original run_task.sh evaluation pipeline.
+
+WORKSPACE="/home/agent/workspace"
+LOGS_DIR="/logs/verifier"
+
+mkdir -p "$LOGS_DIR"
+
+echo "=== PostTrainBench Verifier ==="
+echo "Workspace: $WORKSPACE"
+echo "Logs dir: $LOGS_DIR"
+
+# Check GPU availability
+echo ""
+echo "=== GPU Check ==="
+nvidia-smi 2>&1 | tee "$LOGS_DIR/gpu_check.txt" || echo "nvidia-smi failed"
+
+# Check if final_model exists
+echo ""
+echo "=== Checking final_model ==="
+if [ ! -d "$WORKSPACE/final_model" ]; then
+    echo "ERROR: final_model directory not found"
+    ls -la "$WORKSPACE" > "$LOGS_DIR/workspace_listing.txt" 2>&1
+    echo '{"error": "final_model not found", "accuracy": 0}' > "$LOGS_DIR/metrics.json"
+    echo "0" > "$LOGS_DIR/reward.txt"
+    exit 0
+fi
+
+# Check if final_model has required files
+echo "Contents of final_model:"
+ls -la "$WORKSPACE/final_model" | tee "$LOGS_DIR/final_model_listing.txt"
+
+if [ ! -f "$WORKSPACE/final_model/config.json" ]; then
+    echo "ERROR: final_model/config.json not found - not a valid model"
+    echo '{"error": "invalid model - no config.json", "accuracy": 0}' > "$LOGS_DIR/metrics.json"
+    echo "0" > "$LOGS_DIR/reward.txt"
+    exit 0
+fi
+
+# Show model config
+echo ""
+echo "=== Model config.json ==="
+cat "$WORKSPACE/final_model/config.json" | head -50 | tee "$LOGS_DIR/model_config.txt"
+
+# Check for tokenizer
+echo ""
+echo "=== Checking tokenizer files ==="
+ls -la "$WORKSPACE/final_model/"*token* 2>/dev/null || echo "No tokenizer files found with 'token' in name"
+ls -la "$WORKSPACE/final_model/"*.json 2>/dev/null || echo "No json files found"
+
+# ============================================================
+# Read metadata for benchmark and model info
+# ============================================================
+BENCHMARK_ID=""
+BENCHMARK_NAME=""
+MODEL_ID=""
+
+if [ -f "$WORKSPACE/metadata.json" ]; then
+    BENCHMARK_ID=$(python3 -c "import json; print(json.load(open('$WORKSPACE/metadata.json'))['benchmark_id'])" 2>/dev/null || echo "")
+    BENCHMARK_NAME=$(python3 -c "import json; print(json.load(open('$WORKSPACE/metadata.json'))['benchmark_name'])" 2>/dev/null || echo "Unknown")
+    MODEL_ID=$(python3 -c "import json; print(json.load(open('$WORKSPACE/metadata.json'))['model_id'])" 2>/dev/null || echo "Unknown")
+    echo "Benchmark ID: $BENCHMARK_ID"
+    echo "Benchmark Name: $BENCHMARK_NAME"
+    echo "Model: $MODEL_ID"
+fi
+
+# ============================================================
+# Run contamination judge (codex CLI)
+# Matches run_task.sh lines 180-201
+# ============================================================
+echo ""
+echo "=== Running Contamination Judge ==="
+
+if [ -f "$WORKSPACE/contamination_judge.py" ] && [ -n "$BENCHMARK_NAME" ]; then
+    # Generate the judge prompt
+    JUDGE_TASK=$(python3 "$WORKSPACE/contamination_judge.py" \
+        --model "$MODEL_ID" \
+        --benchmark "$BENCHMARK_NAME" 2>/dev/null) || true
+
+    if [ -n "$JUDGE_TASK" ] && [ -n "$CODEX_API_KEY" ]; then
+        echo "Running codex CLI contamination judge..."
+        set +e
+        cd "$WORKSPACE"
+        codex --search -a never exec --json -c model_reasoning_summary=detailed \
+            --skip-git-repo-check --yolo --model "gpt-5.1-codex" "$JUDGE_TASK" \
+            2>&1 | tee "$LOGS_DIR/judge_output.json"
+        JUDGE_EXIT_CODE=$?
+        set -e
+        echo "Judge exit code: $JUDGE_EXIT_CODE"
+
+        # Codex writes these files directly into the workspace
+        if [ -f "$WORKSPACE/contamination_judgement.txt" ]; then
+            cp "$WORKSPACE/contamination_judgement.txt" "$LOGS_DIR/contamination_judgement.txt"
+            echo "Contamination judgement: $(cat "$LOGS_DIR/contamination_judgement.txt")"
+        else
+            echo "no contamination detected (codex did not produce output)" > "$LOGS_DIR/contamination_judgement.txt"
+        fi
+        if [ -f "$WORKSPACE/disallowed_model_judgement.txt" ]; then
+            cp "$WORKSPACE/disallowed_model_judgement.txt" "$LOGS_DIR/disallowed_model_judgement.txt"
+            echo "Model judgement: $(cat "$LOGS_DIR/disallowed_model_judgement.txt")"
+        else
+            echo "only allowed use detected (codex did not produce output)" > "$LOGS_DIR/disallowed_model_judgement.txt"
+        fi
+    else
+        echo "Warning: CODEX_API_KEY not set or prompt generation failed, skipping judge"
+        echo "no contamination detected (judge skipped - no API key)" > "$LOGS_DIR/contamination_judgement.txt"
+        echo "only allowed use detected (judge skipped - no API key)" > "$LOGS_DIR/disallowed_model_judgement.txt"
+    fi
+else
+    echo "Warning: contamination_judge.py or metadata not found, skipping judge"
+    echo "no contamination detected (judge not available)" > "$LOGS_DIR/contamination_judgement.txt"
+    echo "only allowed use detected (judge not available)" > "$LOGS_DIR/disallowed_model_judgement.txt"
+fi
+
+# ============================================================
+# Evaluation with 3-phase retry logic
+# Matches run_task.sh evaluation pipeline
+# ============================================================
+echo ""
+echo "=== Running evaluation on final_model ==="
+
+cd "$WORKSPACE"
+
+EVAL_COUNTER=0
+
+kill_gpu_processes() {
+    echo "Killing GPU processes..."
+    # Kill GPU-holding processes EXCEPT PID 1 (container init / dumb-init).
+    # In Docker/Modal, the agent's vLLM process can get reparented to PID 1,
+    # which still holds GPU memory when the verifier starts. Killing PID 1
+    # would destroy the entire container.
+    nvidia-smi --query-compute-apps=pid --format=csv,noheader 2>/dev/null \
+        | grep -v '^$' \
+        | while read pid; do
+            if [ "$pid" -gt 1 ] 2>/dev/null; then
+                kill -9 "$pid" 2>/dev/null || true
+            fi
+        done
+    sleep 5
+}
+
+run_evaluation() {
+    local max_tokens_arg="$1"
+    local eval_num="$2"
+
+    kill_gpu_processes
+
+    set +e
+    python3 evaluate.py \
+        --model-path final_model \
+        --json-output-file "$LOGS_DIR/metrics.json" \
+        --templates-dir templates/ \
+        --limit -1 \
+        ${max_tokens_arg} \
+        2>&1 | tee "$LOGS_DIR/final_eval_${eval_num}.txt"
+    local exit_code=$?
+    set -e
+    return $exit_code
+}
+
+run_evaluation_with_retry() {
+    local max_retries="$1"
+    local max_tokens_arg="$2"
+
+    for ((attempt=1; attempt<=max_retries; attempt++)); do
+        sleep 5
+        if [ -f "$LOGS_DIR/metrics.json" ]; then
+            return 0
+        fi
+
+        EVAL_COUNTER=$((EVAL_COUNTER + 1))
+        echo "Evaluation attempt $EVAL_COUNTER (phase attempt $attempt of $max_retries)"
+
+        run_evaluation "$max_tokens_arg" "$EVAL_COUNTER"
+
+        if [ -f "$LOGS_DIR/metrics.json" ]; then
+            return 0
+        fi
+    done
+
+    return 1
+}
+
+# Determine token limit args per benchmark for phase 2 and 3
+get_phase2_tokens() {
+    case "$BENCHMARK_ID" in
+        aime2025)    echo "--max-tokens 12000" ;;
+        arenahardwriting) echo "--max-new-tokens 12288" ;;
+        bfcl)        echo "--max-tokens 12000" ;;
+        gpqamain)    echo "--max-tokens 12000" ;;
+        gsm8k)       echo "--max-tokens 3000" ;;
+        healthbench) echo "--max-new-tokens 12288" ;;
+        humaneval)   echo "--max-tokens 3000" ;;
+        *)           echo "" ;;
+    esac
+}
+
+get_phase3_tokens() {
+    case "$BENCHMARK_ID" in
+        aime2025)    echo "--max-tokens 8000" ;;
+        arenahardwriting) echo "--max-new-tokens 8192" ;;
+        bfcl)        echo "--max-tokens 8000" ;;
+        gpqamain)    echo "--max-tokens 8000" ;;
+        gsm8k)       echo "--max-tokens 2000" ;;
+        healthbench) echo "--max-new-tokens 8192" ;;
+        humaneval)   echo "--max-tokens 2000" ;;
+        *)           echo "" ;;
+    esac
+}
+
+# Phase 1: up to 4 attempts with default tokens
+echo ""
+echo "--- Phase 1: default token limits (up to 4 attempts) ---"
+run_evaluation_with_retry 4 ""
+
+# Phase 2: up to 3 attempts with reduced tokens
+PHASE2_TOKENS=$(get_phase2_tokens)
+echo ""
+echo "--- Phase 2: reduced tokens [${PHASE2_TOKENS}] (up to 3 attempts) ---"
+run_evaluation_with_retry 3 "$PHASE2_TOKENS"
+
+# Phase 3: up to 2 attempts with further reduced tokens
+PHASE3_TOKENS=$(get_phase3_tokens)
+echo ""
+echo "--- Phase 3: further reduced tokens [${PHASE3_TOKENS}] (up to 2 attempts) ---"
+run_evaluation_with_retry 2 "$PHASE3_TOKENS"
+
+# ============================================================
+# Extract accuracy and write reward
+# ============================================================
+echo ""
+echo "=== Evaluation complete (${EVAL_COUNTER} total attempts) ==="
+
+if [ -f "$LOGS_DIR/metrics.json" ]; then
+    echo "metrics.json contents:"
+    cat "$LOGS_DIR/metrics.json"
+
+    # Try to extract accuracy from the metrics JSON
+    ACCURACY=$(python3 -c "
+import json
+try:
+    with open('$LOGS_DIR/metrics.json', 'r') as f:
+        metrics = json.load(f)
+    # Try common metric names
+    for key in ['accuracy', 'pass@1', 'score', 'exact_match']:
+        if key in metrics:
+            print(metrics[key])
+            break
+    else:
+        # If no known metric, use first numeric value
+        for v in metrics.values():
+            if isinstance(v, (int, float)):
+                print(v)
+                break
+        else:
+            print(0)
+except Exception as e:
+    print(f'Error parsing metrics: {e}', file=__import__('sys').stderr)
+    print(0)
+" 2>&1)
+
+    echo "Accuracy: $ACCURACY"
+    echo "$ACCURACY" > "$LOGS_DIR/reward.txt"
+else
+    echo "ERROR: metrics.json not created after all evaluation attempts"
+    echo "0" > "$LOGS_DIR/reward.txt"
+fi
+
+echo ""
+echo "=== Verification complete ==="
+echo "Results in $LOGS_DIR/"
+ls -la "$LOGS_DIR/"
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..ecfc1ad
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1084 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+
+[[package]]
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