{
 "trial": "Corrected re-run: does a CLAUDE.md rule against comments change anything, with the user-level file actually excluded",
 "claude_code": "2.1.226",
 "date": "2026-08-10",
 "model": "claude-haiku-4-5-20251001",
 "supersedes": "The Haiku arm of /only-opus-added-docstrings-and-claude-md-stopped-it-8-of-8/, whose control arm was contaminated by a machine-level CLAUDE.md forbidding comments.",
 "isolation": {
  "flag": "--setting-sources project",
  "verified_how": "An InstructionsLoaded hook logged memory_type for each load. Default: user=1, project=1. With the flag: user=0, project=1. Tools still work and files are still created.",
  "rejected_alternative": "--bare also blocks user CLAUDE.md discovery but disables enough tooling that no file is created at all, so a zero under --bare is a failed measurement rather than an absence."
 },
 "arms": {
  "none": "No project CLAUDE.md and no user-level CLAUDE.md. A genuine control.",
  "forbid": "Project CLAUDE.md forbidding comments and docstrings, user-level still excluded."
 },
 "rounds": 2,
 "files": [
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 0,
   "file": "durations.py",
   "task": "Create durations.py with a function that parses a duration string like '1h30m45s' into total seconds, handling missing units.",
   "comments": 0,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1244,
   "code": "import re\n\n\ndef parse_duration(duration_str: str) -> int:\n    \"\"\"\n    Parse a duration string into total seconds.\n\n    Accepts formats like '1h30m45s', '30m', '45s', '2h', etc.\n    Units can be in any order and can be omitted.\n\n    Args:\n        duration_str: Duration string with units (h=hours, m=minutes, s=seconds)\n\n    Returns:\n        Total duration in seconds\n\n    Raises:\n        ValueError: If format is invalid or contains duplicate units\n    \"\"\"\n    duration_str = duration_str.strip()\n    if not duration_str:\n        raise ValueError(\"Duration string cannot be empty\")\n\n    pattern = r'(\\d+)\\s*([hms])'\n    matches = re.findall(pattern, duration_str)\n\n    if not matches:\n        raise ValueError(f\"Invalid duration format: '{duration_str}'\")\n\n    total_seconds = 0\n    seen_units = set()\n\n    for value_str, unit in matches:\n        if unit in seen_units:\n            raise ValueError(f\"Duplicate unit '{unit}' in duration string: '{duration_str}'\")\n        seen_units.add(unit)\n\n        value = int(value_str)\n        if unit == 'h':\n            total_seconds += value * 3600\n        elif unit == 'm':\n            total_seconds += value * 60\n        elif unit == 's':\n            total_seconds += value\n\n    return total_seconds\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 1,
   "file": "retry.py",
   "task": "Create retry.py with a function that retries a callable up to n times with exponential backoff and re-raises the last error.",
   "comments": 0,
   "docstrings": 2,
   "clean": false,
   "status": "ok",
   "bytes": 2116,
   "code": "import time\nimport functools\nfrom typing import Callable, Any, Optional, Type, Tuple\n\n\ndef retry(\n    func: Callable,\n    max_attempts: int = 3,\n    initial_delay: float = 1.0,\n    max_delay: float = 60.0,\n    backoff_multiplier: float = 2.0,\n    exceptions: Tuple[Type[Exception], ...] = (Exception,),\n) -> Any:\n    \"\"\"\n    Retry a callable with exponential backoff.\n\n    Args:\n        func: Callable to retry (should take no arguments)\n        max_attempts: Maximum number of attempts (including first try)\n        initial_delay: Initial delay in seconds before first retry\n        max_delay: Maximum delay in seconds between retries\n        backoff_multiplier: Multiplier for exponential backoff\n        exceptions: Tuple of exception types to catch\n\n    Returns:\n        Return value of func if successful\n\n    Raises:\n        Last exception encountered if all attempts fail\n    \"\"\"\n    last_exception = None\n    delay = initial_delay\n\n    for attempt in range(max_attempts):\n        try:\n            return func()\n        except exceptions as e:\n            last_exception = e\n            if attempt < max_attempts - 1:\n                time.sleep(delay)\n                delay = min(delay * backoff_multiplier, max_delay)\n\n    raise last_exception\n\n\ndef retry_decorator(\n    max_attempts: int = 3,\n    initial_delay: float = 1.0,\n    max_delay: float = 60.0,\n    backoff_multiplier: float = 2.0,\n    exceptions: Tuple[Type[Exception], ...] = (Exception,),\n):\n    \"\"\"\n    Decorator for retrying a function with exponential backoff.\n\n    Usage:\n        @retry_decorator(max_attempts=5)\n        def flaky_operation():\n            ...\n    \"\"\"\n    def decorator(func: Callable) -> Callable:\n        @functools.wraps(func)\n        def wrapper(*args, **kwargs) -> Any:\n            return retry(\n                lambda: func(*args, **kwargs),\n                max_attempts=max_attempts,\n                initial_delay=initial_delay,\n                max_delay=max_delay,\n                backoff_multiplier=backoff_multiplier,\n                exceptions=exceptions,\n            )\n        return wrapper\n    return decorator\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 2,
   "file": "grouping.py",
   "task": "Create grouping.py with a function that groups a list of dicts by a key and returns a dict of lists.",
   "comments": 0,
   "docstrings": 2,
   "clean": false,
   "status": "ok",
   "bytes": 1623,
   "code": "from typing import List, Dict, Any, Callable, Hashable\n\n\ndef group_by(\n    items: List[Dict[str, Any]],\n    key: str,\n    default: Any = None,\n    keep_missing: bool = False,\n) -> Dict[Any, List[Dict[str, Any]]]:\n    \"\"\"\n    Group a list of dictionaries by a key.\n\n    Args:\n        items: List of dictionaries to group\n        key: Dictionary key to group by\n        default: Default value for items with missing keys (if keep_missing=True)\n        keep_missing: If True, items missing the key are grouped under 'default'\n\n    Returns:\n        Dictionary mapping group values to lists of items\n    \"\"\"\n    result = {}\n\n    for item in items:\n        if key in item:\n            group_key = item[key]\n        elif keep_missing:\n            group_key = default\n        else:\n            continue\n\n        if group_key not in result:\n            result[group_key] = []\n        result[group_key].append(item)\n\n    return result\n\n\ndef group_by_func(\n    items: List[Dict[str, Any]],\n    func: Callable[[Dict[str, Any]], Hashable],\n) -> Dict[Any, List[Dict[str, Any]]]:\n    \"\"\"\n    Group a list of dictionaries using a custom function.\n\n    Args:\n        items: List of dictionaries to group\n        func: Function that takes a dict and returns a grouping key\n\n    Returns:\n        Dictionary mapping group values to lists of items\n\n    Example:\n        group_by_func(users, lambda u: u['age'] // 10)  # Group by decade\n    \"\"\"\n    result = {}\n\n    for item in items:\n        group_key = func(item)\n        if group_key not in result:\n            result[group_key] = []\n        result[group_key].append(item)\n\n    return result\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 3,
   "file": "csvload.py",
   "task": "Create csvload.py with a function that reads a CSV file and returns a list of dicts, coercing numeric columns.",
   "comments": 2,
   "docstrings": 4,
   "clean": false,
   "status": "ok",
   "bytes": 1997,
   "code": "import csv\nfrom typing import List, Dict, Any, Optional, Union\n\n\ndef load_csv(\n    filepath: str,\n    numeric_columns: Optional[List[str]] = None,\n    auto_detect_numeric: bool = True,\n) -> List[Dict[str, Any]]:\n    \"\"\"\n    Load a CSV file and return a list of dictionaries.\n\n    Automatically coerces numeric columns to int or float.\n\n    Args:\n        filepath: Path to the CSV file\n        numeric_columns: Explicit list of numeric column names (overrides auto-detect)\n        auto_detect_numeric: If True, auto-detect numeric columns\n\n    Returns:\n        List of dictionaries with rows from the CSV\n    \"\"\"\n    with open(filepath, 'r', encoding='utf-8') as f:\n        reader = csv.DictReader(f)\n        rows = list(reader)\n\n    if not rows:\n        return []\n\n    # Determine numeric columns\n    if numeric_columns is not None:\n        numeric_cols = set(numeric_columns)\n    elif auto_detect_numeric:\n        numeric_cols = _detect_numeric_columns(rows)\n    else:\n        numeric_cols = set()\n\n    # Coerce values\n    for row in rows:\n        for col in numeric_cols:\n            if col in row and row[col]:\n                row[col] = _to_number(row[col])\n\n    return rows\n\n\ndef _detect_numeric_columns(rows: List[Dict[str, str]]) -> set:\n    \"\"\"Auto-detect which columns are numeric.\"\"\"\n    numeric = set()\n\n    for col in rows[0].keys():\n        if all(\n            _can_be_number(row.get(col, '').strip())\n            for row in rows\n            if row.get(col, '').strip()\n        ):\n            numeric.add(col)\n\n    return numeric\n\n\ndef _can_be_number(value: str) -> bool:\n    \"\"\"Check if a string can be converted to a number.\"\"\"\n    try:\n        float(value)\n        return True\n    except ValueError:\n        return False\n\n\ndef _to_number(value: str) -> Union[int, float]:\n    \"\"\"Convert a string to int or float.\"\"\"\n    if '.' not in value and 'e' not in value.lower():\n        try:\n            return int(value)\n        except ValueError:\n            pass\n    return float(value)\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 4,
   "file": "nested.py",
   "task": "Create nested.py with a function that safely gets a nested key path from a dict, returning a default when missing.",
   "comments": 0,
   "docstrings": 3,
   "clean": false,
   "status": "ok",
   "bytes": 3099,
   "code": "from typing import Any, Union, List\n\n\ndef get_nested(\n    data: dict,\n    path: Union[str, List[str]],\n    default: Any = None,\n    separator: str = '.',\n) -> Any:\n    \"\"\"\n    Safely get a nested value from a dict using a key path.\n\n    Args:\n        data: Dictionary to search\n        path: Dot-separated string (e.g., 'a.b.c') or list of keys ['a', 'b', 'c']\n        default: Value to return if path doesn't exist\n        separator: Separator for string paths (default '.')\n\n    Returns:\n        The nested value or default if not found\n\n    Example:\n        data = {'user': {'name': 'Alice', 'age': 30}}\n        get_nested(data, 'user.name')  # 'Alice'\n        get_nested(data, 'user.email')  # None\n        get_nested(data, 'user.email', 'unknown')  # 'unknown'\n    \"\"\"\n    if isinstance(path, str):\n        keys = path.split(separator)\n    else:\n        keys = path\n\n    current = data\n    for key in keys:\n        if not isinstance(current, dict) or key not in current:\n            return default\n        current = current[key]\n\n    return current\n\n\ndef set_nested(\n    data: dict,\n    path: Union[str, List[str]],\n    value: Any,\n    separator: str = '.',\n    create_missing: bool = True,\n) -> dict:\n    \"\"\"\n    Safely set a nested value in a dict, creating intermediate dicts as needed.\n\n    Args:\n        data: Dictionary to modify\n        path: Dot-separated string or list of keys\n        value: Value to set\n        separator: Separator for string paths (default '.')\n        create_missing: If True, create missing intermediate dicts\n\n    Returns:\n        The modified dictionary\n\n    Example:\n        data = {'user': {}}\n        set_nested(data, 'user.profile.name', 'Alice')\n        # data is now {'user': {'profile': {'name': 'Alice'}}}\n    \"\"\"\n    if isinstance(path, str):\n        keys = path.split(separator)\n    else:\n        keys = list(path)\n\n    if not keys:\n        return data\n\n    current = data\n    for key in keys[:-1]:\n        if key not in current:\n            if not create_missing:\n                return data\n            current[key] = {}\n        elif not isinstance(current[key], dict):\n            if not create_missing:\n                return data\n            current[key] = {}\n        current = current[key]\n\n    current[keys[-1]] = value\n    return data\n\n\ndef has_nested(\n    data: dict,\n    path: Union[str, List[str]],\n    separator: str = '.',\n) -> bool:\n    \"\"\"\n    Check if a nested path exists in a dict.\n\n    Args:\n        data: Dictionary to search\n        path: Dot-separated string or list of keys\n        separator: Separator for string paths (default '.')\n\n    Returns:\n        True if path exists, False otherwise\n\n    Example:\n        data = {'user': {'name': 'Alice'}}\n        has_nested(data, 'user.name')  # True\n        has_nested(data, 'user.email')  # False\n    \"\"\"\n    if isinstance(path, str):\n        keys = path.split(separator)\n    else:\n        keys = path\n\n    current = data\n    for key in keys:\n        if not isinstance(current, dict) or key not in current:\n            return False\n        current = current[key]\n\n    return True\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 5,
   "file": "movingavg.py",
   "task": "Create movingavg.py with a function that computes a moving average over a list for a given window size.",
   "comments": 0,
   "docstrings": 2,
   "clean": false,
   "status": "ok",
   "bytes": 1902,
   "code": "from typing import List, Union\n\n\ndef moving_average(\n    data: List[Union[int, float]],\n    window: int,\n) -> List[float]:\n    \"\"\"\n    Compute moving average over a list.\n\n    Returns a list of averages for each sliding window position.\n    The output length is len(data) - window + 1.\n\n    Args:\n        data: List of numeric values\n        window: Window size (must be > 0)\n\n    Returns:\n        List of moving averages\n\n    Raises:\n        ValueError: If window size is invalid\n\n    Example:\n        moving_average([1, 2, 3, 4, 5], 3)\n        # [2.0, 3.0, 4.0]\n    \"\"\"\n    if window <= 0:\n        raise ValueError(f\"Window size must be positive, got {window}\")\n    if window > len(data):\n        raise ValueError(f\"Window size {window} exceeds data length {len(data)}\")\n\n    result = []\n    for i in range(window - 1, len(data)):\n        window_values = data[i - window + 1 : i + 1]\n        avg = sum(window_values) / window\n        result.append(avg)\n\n    return result\n\n\ndef exponential_moving_average(\n    data: List[Union[int, float]],\n    alpha: float = 0.3,\n) -> List[float]:\n    \"\"\"\n    Compute exponential moving average over a list.\n\n    Gives more weight to recent values. First value equals first data point.\n\n    Args:\n        data: List of numeric values\n        alpha: Smoothing factor (0 < alpha <= 1, higher = more weight to recent)\n\n    Returns:\n        List of exponential moving averages (same length as input)\n\n    Raises:\n        ValueError: If alpha is invalid\n\n    Example:\n        exponential_moving_average([1, 2, 3, 4, 5], alpha=0.3)\n        # [1.0, 1.3, 1.91, 2.537, 3.2759]\n    \"\"\"\n    if not (0 < alpha <= 1):\n        raise ValueError(f\"Alpha must be in (0, 1], got {alpha}\")\n    if not data:\n        return []\n\n    result = [float(data[0])]\n    for value in data[1:]:\n        ema = alpha * value + (1 - alpha) * result[-1]\n        result.append(ema)\n\n    return result\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 6,
   "file": "emailcheck.py",
   "task": "Create emailcheck.py with a function that validates an email address and returns a reason when invalid.",
   "comments": 7,
   "docstrings": 3,
   "clean": false,
   "status": "ok",
   "bytes": 3709,
   "code": "import re\nfrom typing import Dict, Tuple, Union\n\n\ndef validate_email(email: str) -> Dict[str, Union[bool, str, None]]:\n    \"\"\"\n    Validate an email address and return validation status with reason if invalid.\n\n    Args:\n        email: Email address to validate\n\n    Returns:\n        Dict with 'valid' (bool) and 'reason' (str if invalid, None if valid)\n\n    Example:\n        validate_email('user@example.com')\n        # {'valid': True, 'reason': None}\n\n        validate_email('invalid.email')\n        # {'valid': False, 'reason': 'Email missing @ symbol'}\n    \"\"\"\n    email = email.strip() if email else \"\"\n\n    # Length checks\n    if not email:\n        return {\"valid\": False, \"reason\": \"Email is empty\"}\n\n    if len(email) > 254:\n        return {\"valid\": False, \"reason\": \"Email exceeds 254 characters\"}\n\n    # Basic format checks\n    if \" \" in email:\n        return {\"valid\": False, \"reason\": \"Email contains spaces\"}\n\n    at_count = email.count(\"@\")\n    if at_count == 0:\n        return {\"valid\": False, \"reason\": \"Email missing @ symbol\"}\n    if at_count > 1:\n        return {\"valid\": False, \"reason\": \"Email has multiple @ symbols\"}\n\n    # Split local and domain\n    local, domain = email.rsplit(\"@\", 1)\n\n    if not local:\n        return {\"valid\": False, \"reason\": \"Email missing local part (before @)\"}\n    if not domain:\n        return {\"valid\": False, \"reason\": \"Email missing domain (after @)\"}\n\n    # Validate local part\n    if len(local) > 64:\n        return {\"valid\": False, \"reason\": \"Local part exceeds 64 characters\"}\n\n    if local.startswith(\".\") or local.endswith(\".\"):\n        return {\"valid\": False, \"reason\": \"Local part cannot start or end with dot\"}\n\n    if \"..\" in local:\n        return {\"valid\": False, \"reason\": \"Local part cannot have consecutive dots\"}\n\n    local_pattern = r\"^[a-zA-Z0-9._+-]+$\"\n    if not re.match(local_pattern, local):\n        return {\"valid\": False, \"reason\": \"Local part contains invalid characters\"}\n\n    # Validate domain\n    if \".\" not in domain:\n        return {\"valid\": False, \"reason\": \"Domain missing TLD (e.g., .com)\"}\n\n    domain_pattern = r\"^[a-zA-Z0-9.-]+$\"\n    if not re.match(domain_pattern, domain):\n        return {\"valid\": False, \"reason\": \"Domain contains invalid characters\"}\n\n    if domain.startswith(\".\") or domain.endswith(\".\"):\n        return {\"valid\": False, \"reason\": \"Domain cannot start or end with dot\"}\n\n    if \"--\" in domain:\n        return {\"valid\": False, \"reason\": \"Domain contains consecutive hyphens\"}\n\n    # Validate domain parts\n    parts = domain.split(\".\")\n    for part in parts:\n        if not part:\n            return {\"valid\": False, \"reason\": \"Domain has empty parts\"}\n        if part.startswith(\"-\") or part.endswith(\"-\"):\n            return {\"valid\": False, \"reason\": \"Domain parts cannot start or end with hyphen\"}\n\n    # Validate TLD\n    tld = parts[-1]\n    if len(tld) < 2:\n        return {\"valid\": False, \"reason\": \"TLD must be at least 2 characters\"}\n    if not tld.isalpha():\n        return {\"valid\": False, \"reason\": \"TLD must contain only letters\"}\n\n    return {\"valid\": True, \"reason\": None}\n\n\ndef is_valid_email(email: str) -> bool:\n    \"\"\"\n    Quick check if an email is valid (returns only boolean).\n\n    Args:\n        email: Email address to validate\n\n    Returns:\n        True if valid, False otherwise\n    \"\"\"\n    return validate_email(email)[\"valid\"]\n\n\ndef validate_email_with_reason(email: str) -> Tuple[bool, str]:\n    \"\"\"\n    Validate an email and return (is_valid, reason) tuple.\n\n    Args:\n        email: Email address to validate\n\n    Returns:\n        Tuple of (is_valid, reason_string)\n    \"\"\"\n    result = validate_email(email)\n    return result[\"valid\"], result[\"reason\"] or \"Valid email\"\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 7,
   "file": "slugify.py",
   "task": "Create slugify.py with a function that converts a title into a url slug, collapsing separators.",
   "comments": 4,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1613,
   "code": "import re\nimport unicodedata\n\n\ndef slugify(\n    text: str,\n    separator: str = \"-\",\n    lowercase: bool = True,\n    remove_accents: bool = True,\n) -> str:\n    \"\"\"\n    Convert text into a URL-safe slug.\n\n    Removes special characters, collapses separators, and strips leading/trailing\n    separators. Optionally converts to lowercase and removes accents.\n\n    Args:\n        text: Text to convert\n        separator: Character to use as separator (default '-')\n        lowercase: If True, convert to lowercase (default True)\n        remove_accents: If True, remove accented characters (default True)\n\n    Returns:\n        Slugified text (URL-safe)\n\n    Example:\n        slugify('Hello World!')  # 'hello-world'\n        slugify('  Multiple   Spaces  ')  # 'multiple-spaces'\n        slugify('Caf\u00e9')  # 'cafe'\n        slugify('Python 3.9+')  # 'python-39'\n        slugify('My_Post', separator='_')  # 'my_post'\n    \"\"\"\n    if not text:\n        return \"\"\n\n    text = text.strip()\n\n    if lowercase:\n        text = text.lower()\n\n    if remove_accents:\n        # Remove accented characters via NFD normalization\n        text = \"\".join(\n            c\n            for c in unicodedata.normalize(\"NFD\", text)\n            if unicodedata.category(c) != \"Mn\"\n        )\n\n    # Replace non-alphanumeric characters with separator\n    escaped_sep = re.escape(separator)\n    text = re.sub(f\"[^a-zA-Z0-9{escaped_sep}]\", separator, text)\n\n    # Collapse multiple consecutive separators\n    text = re.sub(f\"{escaped_sep}+\", separator, text)\n\n    # Strip separators from start and end\n    text = text.strip(separator)\n\n    return text\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 0,
   "file": "durations.py",
   "task": "Create durations.py with a function that parses a duration string like '1h30m45s' into total seconds, handling missing units.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 274,
   "code": "import re\n\n\ndef parse_duration(duration_str):\n    total_seconds = 0\n    units = {'h': 3600, 'm': 60, 's': 1}\n    pattern = r'(\\d+)([hms])'\n\n    for value, unit in re.findall(pattern, duration_str):\n        total_seconds += int(value) * units[unit]\n\n    return total_seconds\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 1,
   "file": "retry.py",
   "task": "Create retry.py with a function that retries a callable up to n times with exponential backoff and re-raises the last error.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 451,
   "code": "import time\n\n\ndef retry(func, *args, max_attempts=3, initial_delay=1, backoff_factor=2, **kwargs):\n    last_exception = None\n    delay = initial_delay\n\n    for attempt in range(max_attempts):\n        try:\n            return func(*args, **kwargs)\n        except Exception as e:\n            last_exception = e\n            if attempt < max_attempts - 1:\n                time.sleep(delay)\n                delay *= backoff_factor\n\n    raise last_exception\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 2,
   "file": "grouping.py",
   "task": "Create grouping.py with a function that groups a list of dicts by a key and returns a dict of lists.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 181,
   "code": "from collections import defaultdict\n\n\ndef group_by(items, key):\n    groups = defaultdict(list)\n    for item in items:\n        groups[item[key]].append(item)\n    return dict(groups)\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 3,
   "file": "csvload.py",
   "task": "Create csvload.py with a function that reads a CSV file and returns a list of dicts, coercing numeric columns.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 977,
   "code": "import csv\nfrom pathlib import Path\n\n\ndef load_csv(filepath):\n    filepath = Path(filepath)\n\n    with open(filepath, newline='') as f:\n        reader = csv.DictReader(f)\n        rows = list(reader)\n\n    if not rows:\n        return []\n\n    numeric_columns = set()\n    for key in rows[0].keys():\n        is_numeric = True\n        for row in rows:\n            value = row[key]\n            if value and not _is_numeric(value):\n                is_numeric = False\n                break\n        if is_numeric:\n            numeric_columns.add(key)\n\n    for row in rows:\n        for key in numeric_columns:\n            if row[key]:\n                row[key] = _coerce_number(row[key])\n\n    return rows\n\n\ndef _is_numeric(value):\n    try:\n        float(value)\n        return True\n    except ValueError:\n        return False\n\n\ndef _coerce_number(value):\n    try:\n        if '.' in value:\n            return float(value)\n        return int(value)\n    except ValueError:\n        return value\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 4,
   "file": "nested.py",
   "task": "Create nested.py with a function that safely gets a nested key path from a dict, returning a default when missing.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 297,
   "code": "def get_nested(data, path, default=None):\n    if isinstance(path, str):\n        path = path.split('.')\n\n    current = data\n    for key in path:\n        if isinstance(current, dict) and key in current:\n            current = current[key]\n        else:\n            return default\n\n    return current\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 5,
   "file": "movingavg.py",
   "task": "Create movingavg.py with a function that computes a moving average over a list for a given window size.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 231,
   "code": "def moving_average(values, window_size):\n    if window_size < 1 or len(values) < window_size:\n        return []\n\n    return [sum(values[i : i + window_size]) / window_size\n            for i in range(len(values) - window_size + 1)]\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 6,
   "file": "emailcheck.py",
   "task": "Create emailcheck.py with a function that validates an email address and returns a reason when invalid.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 687,
   "code": "import re\n\n\ndef validate_email(email):\n    if not email:\n        return \"Email cannot be empty\"\n\n    if ' ' in email:\n        return \"Email contains spaces\"\n\n    if email.count('@') != 1:\n        return \"Email must contain exactly one @\"\n\n    local, domain = email.split('@')\n\n    if not local or not domain:\n        return \"Email must have both local and domain parts\"\n\n    if '.' not in domain:\n        return \"Email domain must contain a dot\"\n\n    if not re.match(r'^[a-zA-Z0-9._\\-]+$', local):\n        return \"Email local part contains invalid characters\"\n\n    if not re.match(r'^[a-zA-Z0-9.\\-]+$', domain):\n        return \"Email domain contains invalid characters\"\n\n    return None\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 7,
   "file": "slugify.py",
   "task": "Create slugify.py with a function that converts a title into a url slug, collapsing separators.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 181,
   "code": "import re\n\n\ndef slugify(title):\n    slug = title.lower()\n    slug = re.sub(r'[^\\w\\s-]', '', slug)\n    slug = re.sub(r'[-\\s]+', '-', slug)\n    slug = slug.strip('-')\n    return slug\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 0,
   "file": "durations.py",
   "task": "Create durations.py with a function that parses a duration string like '1h30m45s' into total seconds, handling missing units.",
   "comments": 6,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1173,
   "code": "import re\n\n\ndef parse_duration(duration_str):\n    \"\"\"\n    Parse a duration string into total seconds.\n\n    Supports hours (h), minutes (m), and seconds (s).\n    Units can be in any order and missing units are optional.\n\n    Args:\n        duration_str: A duration string like '1h30m45s' or '1h 30m 45s'\n\n    Returns:\n        int: Total duration in seconds\n\n    Raises:\n        ValueError: If the format is invalid or contains unknown units\n    \"\"\"\n    if not duration_str:\n        return 0\n\n    # Convert to lowercase and remove spaces for parsing\n    normalized = duration_str.lower().replace(' ', '')\n\n    # Find all number+unit pairs\n    matches = re.findall(r'(\\d+)([a-z])', normalized)\n\n    if not matches:\n        raise ValueError(f\"Invalid duration format: '{duration_str}'\")\n\n    # Conversion factors to seconds\n    units = {\n        'h': 3600,  # hours\n        'm': 60,    # minutes\n        's': 1,     # seconds\n    }\n\n    total_seconds = 0\n    for value, unit in matches:\n        if unit not in units:\n            raise ValueError(f\"Unknown unit '{unit}' in duration: '{duration_str}'\")\n        total_seconds += int(value) * units[unit]\n\n    return total_seconds\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 1,
   "file": "retry.py",
   "task": "Create retry.py with a function that retries a callable up to n times with exponential backoff and re-raises the last error.",
   "comments": 0,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1404,
   "code": "import time\nfrom typing import Callable, TypeVar, Any\n\nT = TypeVar('T')\n\n\ndef retry(\n    func: Callable[..., T],\n    *args,\n    max_retries: int = 3,\n    initial_delay: float = 1.0,\n    backoff_multiplier: float = 2.0,\n    **kwargs\n) -> T:\n    \"\"\"\n    Retry a callable with exponential backoff.\n\n    Attempts to call the function up to max_retries + 1 times (initial attempt\n    plus retries). If all attempts fail, the last exception is re-raised.\n\n    Args:\n        func: The callable to retry\n        *args: Positional arguments for the callable\n        max_retries: Maximum number of retry attempts (default: 3)\n        initial_delay: Initial delay between retries in seconds (default: 1.0)\n        backoff_multiplier: Multiplier for exponential backoff (default: 2.0)\n        **kwargs: Keyword arguments for the callable\n\n    Returns:\n        The return value of the callable\n\n    Raises:\n        The last exception encountered after all retries are exhausted\n\n    Example:\n        result = retry(requests.get, 'https://example.com', max_retries=3)\n    \"\"\"\n    last_error = None\n    delay = initial_delay\n\n    for attempt in range(max_retries + 1):\n        try:\n            return func(*args, **kwargs)\n        except Exception as e:\n            last_error = e\n            if attempt < max_retries:\n                time.sleep(delay)\n                delay *= backoff_multiplier\n\n    raise last_error\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 2,
   "file": "grouping.py",
   "task": "Create grouping.py with a function that groups a list of dicts by a key and returns a dict of lists.",
   "comments": 2,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1483,
   "code": "from typing import List, Dict, Any, Callable, Union\n\n\ndef group_by(\n    items: List[Dict[str, Any]],\n    key: Union[str, Callable[[Dict[str, Any]], Any]]\n) -> Dict[Any, List[Dict[str, Any]]]:\n    \"\"\"\n    Group a list of dicts by a key or callable.\n\n    Args:\n        items: A list of dictionaries to group\n        key: Either a string (dict key name) or a callable that extracts the\n             grouping key from each dict\n\n    Returns:\n        A dictionary where keys are group identifiers and values are lists\n        of dicts from the original list\n\n    Raises:\n        KeyError: If a string key is not found in a dict\n\n    Example:\n        data = [\n            {'name': 'Alice', 'dept': 'eng'},\n            {'name': 'Bob', 'dept': 'sales'},\n            {'name': 'Charlie', 'dept': 'eng'}\n        ]\n\n        # Group by string key\n        group_by(data, 'dept')\n        # Returns: {\n        #     'eng': [{'name': 'Alice', ...}, {'name': 'Charlie', ...}],\n        #     'sales': [{'name': 'Bob', ...}]\n        # }\n\n        # Group by callable\n        group_by(data, lambda x: x['name'][0])  # Group by first letter\n    \"\"\"\n    result = {}\n\n    for item in items:\n        # Extract the grouping key\n        if callable(key):\n            group_key = key(item)\n        else:\n            group_key = item[key]\n\n        # Add item to the appropriate group\n        if group_key not in result:\n            result[group_key] = []\n        result[group_key].append(item)\n\n    return result\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 3,
   "file": "csvload.py",
   "task": "Create csvload.py with a function that reads a CSV file and returns a list of dicts, coercing numeric columns.",
   "comments": 3,
   "docstrings": 2,
   "clean": false,
   "status": "ok",
   "bytes": 1571,
   "code": "import csv\nfrom typing import List, Dict, Any\n\n\ndef load_csv(filepath: str) -> List[Dict[str, Any]]:\n    \"\"\"\n    Read a CSV file and return a list of dicts with numeric coercion.\n\n    Each row becomes a dictionary with column names as keys. Values that can\n    be parsed as integers or floats are automatically converted; others remain\n    as strings. Empty values are preserved as-is.\n\n    Args:\n        filepath: Path to the CSV file\n\n    Returns:\n        A list of dictionaries, one per row\n\n    Example:\n        data = load_csv('data.csv')\n        # If data.csv contains:\n        #   name,age,score\n        #   Alice,30,95.5\n        #   Bob,25,87.0\n        # Returns:\n        # [\n        #     {'name': 'Alice', 'age': 30, 'score': 95.5},\n        #     {'name': 'Bob', 'age': 25, 'score': 87.0}\n        # ]\n    \"\"\"\n    rows = []\n\n    with open(filepath, 'r', encoding='utf-8') as f:\n        reader = csv.DictReader(f)\n        for row in reader:\n            coerced_row = {}\n            for key, value in row.items():\n                coerced_row[key] = _coerce_value(value)\n            rows.append(coerced_row)\n\n    return rows\n\n\ndef _coerce_value(value: str) -> Any:\n    \"\"\"Attempt to coerce a string to int, float, or keep as string.\"\"\"\n    if value == '' or value is None:\n        return value\n\n    # Try parsing as int\n    try:\n        return int(value)\n    except ValueError:\n        pass\n\n    # Try parsing as float\n    try:\n        return float(value)\n    except ValueError:\n        pass\n\n    # Return as string if no numeric conversion worked\n    return value\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 4,
   "file": "nested.py",
   "task": "Create nested.py with a function that safely gets a nested key path from a dict, returning a default when missing.",
   "comments": 0,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1514,
   "code": "from typing import Any\n\n\ndef get_nested(\n    data: dict,\n    path: str,\n    default: Any = None,\n    separator: str = '.'\n) -> Any:\n    \"\"\"\n    Safely get a nested value from a dict using a dot-separated path.\n\n    Traverses the dictionary following the path keys. If any key is missing\n    or if a non-dict value is encountered mid-path, returns the default value\n    without raising an exception.\n\n    Args:\n        data: The dictionary to search\n        path: Dot-separated path to the value (e.g., 'user.profile.name')\n        default: Value to return if the path doesn't exist (default: None)\n        separator: Path separator character (default: '.')\n\n    Returns:\n        The value at the path, or default if not found or path is invalid\n\n    Example:\n        data = {\n            'user': {\n                'profile': {\n                    'name': 'Alice',\n                    'age': 30\n                }\n            }\n        }\n\n        get_nested(data, 'user.profile.name')\n        # Returns: 'Alice'\n\n        get_nested(data, 'user.profile.email')\n        # Returns: None\n\n        get_nested(data, 'user.settings.theme', default='light')\n        # Returns: 'light'\n\n        get_nested(data, 'user.profile.name', separator='/')\n        # Returns: None (wrong separator)\n    \"\"\"\n    keys = path.split(separator)\n    current = data\n\n    for key in keys:\n        if isinstance(current, dict) and key in current:\n            current = current[key]\n        else:\n            return default\n\n    return current\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 5,
   "file": "movingavg.py",
   "task": "Create movingavg.py with a function that computes a moving average over a list for a given window size.",
   "comments": 0,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1175,
   "code": "from typing import List, Union\n\n\ndef moving_average(values: List[Union[int, float]], window_size: int) -> List[float]:\n    \"\"\"\n    Compute a moving average over a list.\n\n    Slides a window of the specified size across the list and computes the\n    average for each position. Returns only positions where a full window\n    is available.\n\n    Args:\n        values: A list of numeric values\n        window_size: The size of the sliding window (must be >= 1)\n\n    Returns:\n        A list of moving averages\n\n    Raises:\n        ValueError: If window_size is less than 1\n\n    Example:\n        moving_average([1, 2, 3, 4, 5], 2)\n        # Returns: [1.5, 2.5, 3.5, 4.5]\n\n        moving_average([1, 2, 3, 4, 5], 3)\n        # Returns: [2.0, 3.0, 4.0]\n\n        moving_average([10, 20, 30, 40], 1)\n        # Returns: [10.0, 20.0, 30.0, 40.0]\n    \"\"\"\n    if window_size < 1:\n        raise ValueError(\"window_size must be >= 1\")\n\n    if window_size > len(values):\n        return []\n\n    averages = []\n    for i in range(len(values) - window_size + 1):\n        window = values[i : i + window_size]\n        avg = sum(window) / len(window)\n        averages.append(avg)\n\n    return averages\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 6,
   "file": "emailcheck.py",
   "task": "Create emailcheck.py with a function that validates an email address and returns a reason when invalid.",
   "comments": 2,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 2179,
   "code": "import re\nfrom typing import Dict\n\n\ndef validate_email(email: str) -> Dict:\n    \"\"\"\n    Validate an email address and return validation status with reason.\n\n    Checks for basic email structure: local@domain.tld\n    No DNS lookups or SMTP verification is performed.\n\n    Args:\n        email: The email address to validate\n\n    Returns:\n        A dict with keys:\n        - 'valid' (bool): Whether the email is valid\n        - 'reason' (str): Empty string if valid, error message if invalid\n\n    Example:\n        validate_email('user@example.com')\n        # Returns: {'valid': True, 'reason': ''}\n\n        validate_email('invalid.email')\n        # Returns: {'valid': False, 'reason': 'Email must contain exactly one @ symbol'}\n\n        validate_email('user@example')\n        # Returns: {'valid': False, 'reason': 'Domain must contain at least one dot'}\n    \"\"\"\n    email = email.strip()\n\n    if not email:\n        return {'valid': False, 'reason': 'Email is empty'}\n\n    if ' ' in email:\n        return {'valid': False, 'reason': 'Email contains spaces'}\n\n    if email.count('@') != 1:\n        return {'valid': False, 'reason': 'Email must contain exactly one @ symbol'}\n\n    local, domain = email.split('@')\n\n    if not local:\n        return {'valid': False, 'reason': 'Local part (before @) is empty'}\n\n    if not domain:\n        return {'valid': False, 'reason': 'Domain part (after @) is empty'}\n\n    if '.' not in domain:\n        return {'valid': False, 'reason': 'Domain must contain at least one dot'}\n\n    if domain.startswith('.') or domain.endswith('.'):\n        return {'valid': False, 'reason': 'Domain cannot start or end with a dot'}\n\n    if '..' in domain:\n        return {'valid': False, 'reason': 'Domain cannot contain consecutive dots'}\n\n    # Check TLD is at least 2 characters\n    tld = domain.split('.')[-1]\n    if len(tld) < 2:\n        return {'valid': False, 'reason': 'Top-level domain must be at least 2 characters'}\n\n    # Check for valid characters\n    if not re.match(r'^[a-zA-Z0-9.!#$%&\\'*+/=?^_`{|}~-]+@[a-zA-Z0-9.-]+$', email):\n        return {'valid': False, 'reason': 'Email contains invalid characters'}\n\n    return {'valid': True, 'reason': ''}\n",
   "isolated": null
  },
  {
   "arm": "none",
   "model": "claude-haiku-4-5-20251001",
   "turn": 7,
   "file": "slugify.py",
   "task": "Create slugify.py with a function that converts a title into a url slug, collapsing separators.",
   "comments": 4,
   "docstrings": 1,
   "clean": false,
   "status": "ok",
   "bytes": 1361,
   "code": "import re\n\n\ndef slugify(title: str, separator: str = '-') -> str:\n    \"\"\"\n    Convert a title into a URL slug.\n\n    Converts to lowercase, replaces spaces and special characters with\n    the separator, and collapses multiple consecutive separators into one.\n    Leading and trailing separators are stripped.\n\n    Args:\n        title: The title to convert\n        separator: Character(s) to use as separator (default: '-')\n\n    Returns:\n        A URL-safe slug\n\n    Example:\n        slugify('Hello World')\n        # Returns: 'hello-world'\n\n        slugify('Hello  World')\n        # Returns: 'hello-world'\n\n        slugify('Hello---World')\n        # Returns: 'hello-world'\n\n        slugify('Hello & World!')\n        # Returns: 'hello-world'\n\n        slugify('  Hello World  ')\n        # Returns: 'hello-world'\n\n        slugify('Hello_World', separator='_')\n        # Returns: 'hello_world'\n    \"\"\"\n    slug = title.lower()\n\n    # Replace spaces with separator\n    slug = slug.replace(' ', separator)\n\n    # Replace non-alphanumeric characters (except separator) with separator\n    slug = re.sub(rf'[^a-z0-9{re.escape(separator)}]', separator, slug)\n\n    # Collapse multiple consecutive separators into one\n    slug = re.sub(rf'{re.escape(separator)}+', separator, slug)\n\n    # Strip separators from start and end\n    slug = slug.strip(separator)\n\n    return slug\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 0,
   "file": "durations.py",
   "task": "Create durations.py with a function that parses a duration string like '1h30m45s' into total seconds, handling missing units.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 409,
   "code": "import re\n\n\ndef parse_duration(duration_str):\n    pattern = r'(\\d+)([hms])'\n    matches = re.findall(pattern, duration_str.strip().lower())\n\n    if not matches:\n        raise ValueError(f\"Invalid duration format: {duration_str}\")\n\n    total_seconds = 0\n    unit_map = {'h': 3600, 'm': 60, 's': 1}\n\n    for value, unit in matches:\n        total_seconds += int(value) * unit_map[unit]\n\n    return total_seconds\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 1,
   "file": "retry.py",
   "task": "Create retry.py with a function that retries a callable up to n times with exponential backoff and re-raises the last error.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 392,
   "code": "import time\n\n\ndef retry(func, max_retries=3, base_delay=1, multiplier=2):\n    last_error = None\n\n    for attempt in range(max_retries + 1):\n        try:\n            return func()\n        except Exception as e:\n            last_error = e\n            if attempt < max_retries:\n                delay = base_delay * (multiplier ** attempt)\n                time.sleep(delay)\n\n    raise last_error\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 2,
   "file": "grouping.py",
   "task": "Create grouping.py with a function that groups a list of dicts by a key and returns a dict of lists.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 185,
   "code": "from collections import defaultdict\n\n\ndef group_by(items, key):\n    result = defaultdict(list)\n    for item in items:\n        result[item.get(key)].append(item)\n    return dict(result)\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 3,
   "file": "csvload.py",
   "task": "Create csvload.py with a function that reads a CSV file and returns a list of dicts, coercing numeric columns.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 488,
   "code": "import csv\n\n\ndef load_csv(filepath):\n    def coerce_numeric(value):\n        if not value:\n            return value\n        try:\n            if '.' in value:\n                return float(value)\n            return int(value)\n        except (ValueError, TypeError):\n            return value\n\n    rows = []\n    with open(filepath, 'r') as f:\n        reader = csv.DictReader(f)\n        for row in reader:\n            rows.append({k: coerce_numeric(v) for k, v in row.items()})\n    return rows\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 4,
   "file": "nested.py",
   "task": "Create nested.py with a function that safely gets a nested key path from a dict, returning a default when missing.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 234,
   "code": "def get_nested(data, path, default=None):\n    current = data\n    for key in path:\n        if isinstance(current, dict) and key in current:\n            current = current[key]\n        else:\n            return default\n    return current\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 5,
   "file": "movingavg.py",
   "task": "Create movingavg.py with a function that computes a moving average over a list for a given window size.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 234,
   "code": "def moving_average(data, window_size):\n    if window_size <= 0 or window_size > len(data):\n        return []\n\n    return [\n        sum(data[i : i + window_size]) / window_size\n        for i in range(len(data) - window_size + 1)\n    ]\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 6,
   "file": "emailcheck.py",
   "task": "Create emailcheck.py with a function that validates an email address and returns a reason when invalid.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 1190,
   "code": "def validate_email(email):\n    if not email or not isinstance(email, str):\n        return \"Email is empty or invalid\"\n\n    email = email.strip()\n\n    if email.count(\"@\") != 1:\n        return \"Email must contain exactly one @ symbol\"\n\n    local, domain = email.split(\"@\")\n\n    if not local or not domain:\n        return \"Email must have both local and domain parts\"\n\n    if \" \" in email:\n        return \"Email contains spaces\"\n\n    if \".\" not in domain:\n        return \"Domain must contain at least one dot\"\n\n    if \"..\" in email or local.startswith(\".\") or local.endswith(\".\"):\n        return \"Invalid dot placement in email\"\n\n    if domain.startswith(\"-\") or domain.endswith(\"-\"):\n        return \"Domain cannot start or end with hyphen\"\n\n    invalid_chars = [c for c in local if not (c.isalnum() or c in \"._-+\")]\n    if invalid_chars:\n        return f\"Local part contains invalid character: {invalid_chars[0]}\"\n\n    for part in domain.split(\".\"):\n        if not part or part.startswith(\"-\") or part.endswith(\"-\"):\n            return \"Invalid domain format\"\n        if not all(c.isalnum() or c == \"-\" for c in part):\n            return \"Domain contains invalid characters\"\n\n    return None\n",
   "isolated": null
  },
  {
   "arm": "forbid",
   "model": "claude-haiku-4-5-20251001",
   "turn": 7,
   "file": "slugify.py",
   "task": "Create slugify.py with a function that converts a title into a url slug, collapsing separators.",
   "comments": 0,
   "docstrings": 0,
   "clean": true,
   "status": "ok",
   "bytes": 144,
   "code": "import re\n\n\ndef slugify(title):\n    slug = title.lower()\n    slug = re.sub(r\"[^a-z0-9]+\", \"-\", slug)\n    slug = slug.strip(\"-\")\n    return slug\n",
   "isolated": null
  }
 ]
}