Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'cost_code': r'^[A-Z]{1,3}-[0-9]{3,6}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'activity_id': r'^[A-Z]{1,3}[0-9]{4,6}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'drawing_number': r'^[A-Z]{1,2}-[0-9]{3}-[A-Z0-9]{2,4}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'specification_section': r'^[0-9]{2}\\s?[0-9]{2}\\s?[0-9]{2}(\\.[0-9]{2})?

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'level_name': r'^(Level|L|FL)\\s?[-_]?\\s?([0-9]{1,3}|B[0-9]|R|G|M)

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'grid_line': r'^[A-Z]\\.?[0-9]?$|^[0-9]{1,2}\\.?[A-Z]?

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'revision': r'^[A-Z]$|^[0-9]{1,2}$|^Rev\\.?\\s?[A-Z0-9]+

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'date_iso': r'^\\d{4}-\\d{2}-\\d{2}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'email': r'^[\\w\\.-]+@[\\w\\.-]+\\.\\w+

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'phone': r'^\\+?[0-9]{1,3}[-.\\s]?[0-9]{3,4}[-.\\s]?[0-9]{4}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n }\n\n def __init__(self):\n self.rules: List[ValidationRule] = []\n self.custom_patterns: Dict[str, str] = {}\n\n def add_regex_rule(self,\n name: str,\n field: str,\n pattern: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add regex validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.REGEX,\n pattern=pattern,\n message=message or f\"Field '{field}' does not match pattern\",\n severity=severity\n ))\n return self\n\n def add_range_rule(self,\n name: str,\n field: str,\n min_value: float = None,\n max_value: float = None,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add numeric range validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.RANGE,\n min_value=min_value,\n max_value=max_value,\n message=message or f\"Field '{field}' out of range [{min_value}, {max_value}]\",\n severity=severity\n ))\n return self\n\n def add_enum_rule(self,\n name: str,\n field: str,\n allowed_values: List[Any],\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add enumeration validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.ENUM,\n allowed_values=allowed_values,\n message=message or f\"Field '{field}' must be one of: {allowed_values}\",\n severity=severity\n ))\n return self\n\n def add_required_rule(self,\n name: str,\n field: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add required field validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.REQUIRED,\n message=message or f\"Field '{field}' is required\",\n severity=severity\n ))\n return self\n\n def add_custom_rule(self,\n name: str,\n field: str,\n func: Callable[[Any], bool],\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add custom validation function.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.CUSTOM,\n custom_func=func,\n message=message or f\"Field '{field}' failed custom validation\",\n severity=severity\n ))\n return self\n\n def add_pattern(self, name: str, pattern: str):\n \"\"\"Add custom pattern for reuse.\"\"\"\n self.custom_patterns[name] = pattern\n\n def use_pattern(self,\n rule_name: str,\n field: str,\n pattern_name: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Use pre-defined or custom pattern.\"\"\"\n\n pattern = self.custom_patterns.get(pattern_name) or self.PATTERNS.get(pattern_name)\n if not pattern:\n raise ValueError(f\"Pattern '{pattern_name}' not found\")\n\n return self.add_regex_rule(rule_name, field, pattern, message, severity)\n\n def validate_record(self, record: Dict[str, Any]) -> List[ValidationResult]:\n \"\"\"Validate a single record against all rules.\"\"\"\n\n results = []\n\n for rule in self.rules:\n if not rule.enabled:\n continue\n\n value = record.get(rule.field)\n result = self._apply_rule(rule, value)\n results.append(result)\n\n return results\n\n def validate_records(self, records: List[Dict[str, Any]]) -> Dict[str, Any]:\n \"\"\"Validate multiple records and return summary.\"\"\"\n\n all_results = []\n error_count = 0\n warning_count = 0\n\n for i, record in enumerate(records):\n record_results = self.validate_record(record)\n for result in record_results:\n if not result.is_valid:\n result_dict = {\n 'record_index': i,\n 'field': result.field,\n 'message': result.message,\n 'severity': result.severity.value,\n 'value': result.value\n }\n all_results.append(result_dict)\n\n if result.severity == Severity.ERROR:\n error_count += 1\n elif result.severity == Severity.WARNING:\n warning_count += 1\n\n return {\n 'total_records': len(records),\n 'valid_records': len(records) - len(set(r['record_index'] for r in all_results if r['severity'] == 'error')),\n 'error_count': error_count,\n 'warning_count': warning_count,\n 'issues': all_results\n }\n\n def _apply_rule(self, rule: ValidationRule, value: Any) -> ValidationResult:\n \"\"\"Apply single validation rule.\"\"\"\n\n if rule.rule_type == RuleType.REQUIRED:\n is_valid = value is not None and value != \"\" and value != []\n return ValidationResult(\n field=rule.field,\n is_valid=is_valid,\n message=\"\" if is_valid else rule.message,\n severity=rule.severity,\n value=value\n )\n\n # Skip other validations if value is None/empty\n if value is None or value == \"\":\n return ValidationResult(\n field=rule.field,\n is_valid=True,\n message=\"\",\n severity=rule.severity,\n value=value\n )\n\n if rule.rule_type == RuleType.REGEX:\n is_valid = bool(re.match(rule.pattern, str(value)))\n\n elif rule.rule_type == RuleType.RANGE:\n try:\n num_value = float(value)\n is_valid = True\n if rule.min_value is not None and num_value \u003c rule.min_value:\n is_valid = False\n if rule.max_value is not None and num_value > rule.max_value:\n is_valid = False\n except (ValueError, TypeError):\n is_valid = False\n\n elif rule.rule_type == RuleType.ENUM:\n is_valid = value in rule.allowed_values\n\n elif rule.rule_type == RuleType.CUSTOM:\n try:\n is_valid = rule.custom_func(value)\n except Exception:\n is_valid = False\n\n else:\n is_valid = True\n\n return ValidationResult(\n field=rule.field,\n is_valid=is_valid,\n message=\"\" if is_valid else rule.message,\n severity=rule.severity,\n value=value\n )\n\n def get_rules_summary(self) -> List[Dict]:\n \"\"\"Get summary of all rules.\"\"\"\n\n return [{\n 'name': r.name,\n 'field': r.field,\n 'type': r.rule_type.value,\n 'severity': r.severity.value,\n 'enabled': r.enabled\n } for r in self.rules]\n\n\n# Construction-specific validators\nclass ConstructionValidators:\n \"\"\"Pre-built validators for construction data.\"\"\"\n\n @staticmethod\n def wbs_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for WBS codes.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"wbs_required\", \"wbs_code\")\n .use_pattern(\"wbs_format\", \"wbs_code\", \"wbs_code\", \"Invalid WBS format (expected: XX.XX.XX)\")\n )\n\n @staticmethod\n def cost_item_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for cost items.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"code_required\", \"cost_code\")\n .add_required_rule(\"desc_required\", \"description\")\n .use_pattern(\"code_format\", \"cost_code\", \"cost_code\")\n .add_range_rule(\"quantity_positive\", \"quantity\", min_value=0)\n .add_range_rule(\"unit_cost_positive\", \"unit_cost\", min_value=0)\n .add_enum_rule(\"unit_valid\", \"unit\", [\"EA\", \"LF\", \"SF\", \"CY\", \"TON\", \"HR\", \"LS\"])\n )\n\n @staticmethod\n def schedule_activity_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for schedule activities.\"\"\"\n\n def dates_valid(record):\n start = record.get('start_date')\n end = record.get('end_date')\n if start and end:\n return start \u003c= end\n return True\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"id_required\", \"activity_id\")\n .add_required_rule(\"name_required\", \"activity_name\")\n .use_pattern(\"id_format\", \"activity_id\", \"activity_id\")\n .add_range_rule(\"duration_positive\", \"duration\", min_value=0)\n .add_range_rule(\"progress_range\", \"percent_complete\", min_value=0, max_value=100)\n )\n\n @staticmethod\n def bim_element_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for BIM elements.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"guid_required\", \"element_guid\")\n .add_required_rule(\"type_required\", \"element_type\")\n .add_required_rule(\"level_required\", \"level\")\n .use_pattern(\"level_format\", \"level\", \"level_name\", severity=Severity.WARNING)\n .add_enum_rule(\"status_valid\", \"status\",\n [\"New\", \"Existing\", \"Demolished\", \"Temporary\"])\n )\n```\n\n## Quick Start\n\n```python\n# Create validator\nvalidator = ValidationRulesBuilder()\n\n# Add rules\nvalidator.add_required_rule(\"id_required\", \"item_id\")\nvalidator.use_pattern(\"wbs_valid\", \"wbs_code\", \"wbs_code\")\nvalidator.add_range_rule(\"cost_range\", \"total_cost\", min_value=0, max_value=10000000)\nvalidator.add_enum_rule(\"status_valid\", \"status\", [\"Active\", \"Completed\", \"Cancelled\"])\n\n# Validate records\nrecords = [\n {\"item_id\": \"001\", \"wbs_code\": \"01.02.03\", \"total_cost\": 50000, \"status\": \"Active\"},\n {\"item_id\": \"\", \"wbs_code\": \"invalid\", \"total_cost\": -100, \"status\": \"Unknown\"}\n]\n\nresults = validator.validate_records(records)\nprint(f\"Valid: {results['valid_records']}/{results['total_records']}\")\nprint(f\"Errors: {results['error_count']}, Warnings: {results['warning_count']}\")\n```\n\n## Common Use Cases\n\n### 1. Cost Data Validation\n```python\ncost_validator = ConstructionValidators.cost_item_validator()\nresults = cost_validator.validate_records(cost_items)\n```\n\n### 2. Schedule Validation\n```python\nschedule_validator = ConstructionValidators.schedule_activity_validator()\nresults = schedule_validator.validate_records(activities)\n```\n\n### 3. BIM Element Validation\n```python\nbim_validator = ConstructionValidators.bim_element_validator()\nresults = bim_validator.validate_records(elements)\n```\n\n## Resources\n- **DDC Book**: Chapter 2.6 - Data Quality Requirements\n- **Website**: https://datadrivenconstruction.io\n---","attachment_filenames":["claw.json","instructions.md"],"attachments":[{"filename":"claw.json","content":"{\n \"name\": \"validation-rules-builder\",\n \"version\": \"2.0.0\",\n \"description\": \"Build validation rules for construction data. Create RegEx and logic-based validation for BIM elements, cost codes, and schedule data.\",\n \"author\": \"datadrivenconstruction\",\n \"license\": \"MIT\",\n \"permissions\": [\n \"filesystem\"\n ],\n \"entry\": \"instructions.md\",\n \"tags\": [\n \"construction\",\n \"estimation\",\n \"BIM\",\n \"cost-management\",\n \"scheduling\"\n ],\n \"models\": [\n \"claude-*\",\n \"gpt-*\"\n ],\n \"minOpenClawVersion\": \"0.8.0\"\n}","content_type":"application/json; charset=utf-8","language":"json","size":532,"content_sha256":"74318a3abff238143a14cd0dd6900d2fc81d30a51710186a4feffa90d6f31072"},{"filename":"instructions.md","content":"You are a construction industry assistant specializing in construction project management.\n\nBuild validation rules for construction data. Create RegEx and logic-based validation for BIM elements, cost codes, and schedule data.\n\nWhen the user asks to create cost estimates or analyze costs:\n1. Gather the required input data from the user\n2. Process the data using the methods described in SKILL.md\n3. Present results in a clear, structured format\n4. Offer follow-up analysis or export options\n\n## Input Format\n- The user provides project data, file paths, or parameters as described in SKILL.md\n- Accept data in common formats: CSV, Excel, JSON, or direct input\n\n## Output Format\n- Present results in structured tables when applicable\n- Include summary statistics and key findings\n- Offer export to Excel/CSV/JSON when relevant\n\n## Key Reference\n- See SKILL.md for detailed implementation code, classes, and methods\n- Follow the patterns and APIs defined in the skill documentation\n\n## Constraints\n- Only use data provided by the user or referenced in the skill\n- Validate inputs before processing\n- Report errors clearly with suggested fixes\n- Follow construction industry standards and best practices\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":1203,"content_sha256":"26dfc9e07418c837f369f9192da8a76434dce956ddc60eb9ef43a11f6abb608e"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Validation Rules Builder","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Business Case","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Problem Statement","type":"text"}]},{"type":"paragraph","content":[{"text":"Construction data quality challenges:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Inconsistent naming conventions","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Invalid cost codes and WBS","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Missing or malformed data","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Non-compliant BIM elements","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Solution","type":"text"}]},{"type":"paragraph","content":[{"text":"Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Technical Implementation","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"import re\nfrom typing import Dict, Any, List, Optional, Callable\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom datetime import date\n\n\nclass RuleType(Enum):\n REGEX = \"regex\"\n RANGE = \"range\"\n ENUM = \"enum\"\n CUSTOM = \"custom\"\n REQUIRED = \"required\"\n DATE = \"date\"\n REFERENCE = \"reference\"\n\n\nclass Severity(Enum):\n ERROR = \"error\"\n WARNING = \"warning\"\n INFO = \"info\"\n\n\n@dataclass\nclass ValidationResult:\n field: str\n is_valid: bool\n message: str\n severity: Severity\n value: Any = None\n\n\n@dataclass\nclass ValidationRule:\n name: str\n field: str\n rule_type: RuleType\n pattern: str = \"\"\n min_value: float = None\n max_value: float = None\n allowed_values: List[Any] = field(default_factory=list)\n custom_func: Callable = None\n severity: Severity = Severity.ERROR\n message: str = \"\"\n enabled: bool = True\n\n\nclass ValidationRulesBuilder:\n \"\"\"Build and execute validation rules for construction data.\"\"\"\n\n # Pre-defined patterns for construction data\n PATTERNS = {\n 'wbs_code': r'^[0-9]{2}\\.[0-9]{2}\\.[0-9]{2}(\\.[0-9]{2})?

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'cost_code': r'^[A-Z]{1,3}-[0-9]{3,6}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'activity_id': r'^[A-Z]{1,3}[0-9]{4,6}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'drawing_number': r'^[A-Z]{1,2}-[0-9]{3}-[A-Z0-9]{2,4}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'specification_section': r'^[0-9]{2}\\s?[0-9]{2}\\s?[0-9]{2}(\\.[0-9]{2})?

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'level_name': r'^(Level|L|FL)\\s?[-_]?\\s?([0-9]{1,3}|B[0-9]|R|G|M)

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'grid_line': r'^[A-Z]\\.?[0-9]?$|^[0-9]{1,2}\\.?[A-Z]?

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'revision': r'^[A-Z]$|^[0-9]{1,2}$|^Rev\\.?\\s?[A-Z0-9]+

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'date_iso': r'^\\d{4}-\\d{2}-\\d{2}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'email': r'^[\\w\\.-]+@[\\w\\.-]+\\.\\w+

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n 'phone': r'^\\+?[0-9]{1,3}[-.\\s]?[0-9]{3,4}[-.\\s]?[0-9]{4}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---

,\n }\n\n def __init__(self):\n self.rules: List[ValidationRule] = []\n self.custom_patterns: Dict[str, str] = {}\n\n def add_regex_rule(self,\n name: str,\n field: str,\n pattern: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add regex validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.REGEX,\n pattern=pattern,\n message=message or f\"Field '{field}' does not match pattern\",\n severity=severity\n ))\n return self\n\n def add_range_rule(self,\n name: str,\n field: str,\n min_value: float = None,\n max_value: float = None,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add numeric range validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.RANGE,\n min_value=min_value,\n max_value=max_value,\n message=message or f\"Field '{field}' out of range [{min_value}, {max_value}]\",\n severity=severity\n ))\n return self\n\n def add_enum_rule(self,\n name: str,\n field: str,\n allowed_values: List[Any],\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add enumeration validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.ENUM,\n allowed_values=allowed_values,\n message=message or f\"Field '{field}' must be one of: {allowed_values}\",\n severity=severity\n ))\n return self\n\n def add_required_rule(self,\n name: str,\n field: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add required field validation rule.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.REQUIRED,\n message=message or f\"Field '{field}' is required\",\n severity=severity\n ))\n return self\n\n def add_custom_rule(self,\n name: str,\n field: str,\n func: Callable[[Any], bool],\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Add custom validation function.\"\"\"\n\n self.rules.append(ValidationRule(\n name=name,\n field=field,\n rule_type=RuleType.CUSTOM,\n custom_func=func,\n message=message or f\"Field '{field}' failed custom validation\",\n severity=severity\n ))\n return self\n\n def add_pattern(self, name: str, pattern: str):\n \"\"\"Add custom pattern for reuse.\"\"\"\n self.custom_patterns[name] = pattern\n\n def use_pattern(self,\n rule_name: str,\n field: str,\n pattern_name: str,\n message: str = \"\",\n severity: Severity = Severity.ERROR) -> 'ValidationRulesBuilder':\n \"\"\"Use pre-defined or custom pattern.\"\"\"\n\n pattern = self.custom_patterns.get(pattern_name) or self.PATTERNS.get(pattern_name)\n if not pattern:\n raise ValueError(f\"Pattern '{pattern_name}' not found\")\n\n return self.add_regex_rule(rule_name, field, pattern, message, severity)\n\n def validate_record(self, record: Dict[str, Any]) -> List[ValidationResult]:\n \"\"\"Validate a single record against all rules.\"\"\"\n\n results = []\n\n for rule in self.rules:\n if not rule.enabled:\n continue\n\n value = record.get(rule.field)\n result = self._apply_rule(rule, value)\n results.append(result)\n\n return results\n\n def validate_records(self, records: List[Dict[str, Any]]) -> Dict[str, Any]:\n \"\"\"Validate multiple records and return summary.\"\"\"\n\n all_results = []\n error_count = 0\n warning_count = 0\n\n for i, record in enumerate(records):\n record_results = self.validate_record(record)\n for result in record_results:\n if not result.is_valid:\n result_dict = {\n 'record_index': i,\n 'field': result.field,\n 'message': result.message,\n 'severity': result.severity.value,\n 'value': result.value\n }\n all_results.append(result_dict)\n\n if result.severity == Severity.ERROR:\n error_count += 1\n elif result.severity == Severity.WARNING:\n warning_count += 1\n\n return {\n 'total_records': len(records),\n 'valid_records': len(records) - len(set(r['record_index'] for r in all_results if r['severity'] == 'error')),\n 'error_count': error_count,\n 'warning_count': warning_count,\n 'issues': all_results\n }\n\n def _apply_rule(self, rule: ValidationRule, value: Any) -> ValidationResult:\n \"\"\"Apply single validation rule.\"\"\"\n\n if rule.rule_type == RuleType.REQUIRED:\n is_valid = value is not None and value != \"\" and value != []\n return ValidationResult(\n field=rule.field,\n is_valid=is_valid,\n message=\"\" if is_valid else rule.message,\n severity=rule.severity,\n value=value\n )\n\n # Skip other validations if value is None/empty\n if value is None or value == \"\":\n return ValidationResult(\n field=rule.field,\n is_valid=True,\n message=\"\",\n severity=rule.severity,\n value=value\n )\n\n if rule.rule_type == RuleType.REGEX:\n is_valid = bool(re.match(rule.pattern, str(value)))\n\n elif rule.rule_type == RuleType.RANGE:\n try:\n num_value = float(value)\n is_valid = True\n if rule.min_value is not None and num_value \u003c rule.min_value:\n is_valid = False\n if rule.max_value is not None and num_value > rule.max_value:\n is_valid = False\n except (ValueError, TypeError):\n is_valid = False\n\n elif rule.rule_type == RuleType.ENUM:\n is_valid = value in rule.allowed_values\n\n elif rule.rule_type == RuleType.CUSTOM:\n try:\n is_valid = rule.custom_func(value)\n except Exception:\n is_valid = False\n\n else:\n is_valid = True\n\n return ValidationResult(\n field=rule.field,\n is_valid=is_valid,\n message=\"\" if is_valid else rule.message,\n severity=rule.severity,\n value=value\n )\n\n def get_rules_summary(self) -> List[Dict]:\n \"\"\"Get summary of all rules.\"\"\"\n\n return [{\n 'name': r.name,\n 'field': r.field,\n 'type': r.rule_type.value,\n 'severity': r.severity.value,\n 'enabled': r.enabled\n } for r in self.rules]\n\n\n# Construction-specific validators\nclass ConstructionValidators:\n \"\"\"Pre-built validators for construction data.\"\"\"\n\n @staticmethod\n def wbs_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for WBS codes.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"wbs_required\", \"wbs_code\")\n .use_pattern(\"wbs_format\", \"wbs_code\", \"wbs_code\", \"Invalid WBS format (expected: XX.XX.XX)\")\n )\n\n @staticmethod\n def cost_item_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for cost items.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"code_required\", \"cost_code\")\n .add_required_rule(\"desc_required\", \"description\")\n .use_pattern(\"code_format\", \"cost_code\", \"cost_code\")\n .add_range_rule(\"quantity_positive\", \"quantity\", min_value=0)\n .add_range_rule(\"unit_cost_positive\", \"unit_cost\", min_value=0)\n .add_enum_rule(\"unit_valid\", \"unit\", [\"EA\", \"LF\", \"SF\", \"CY\", \"TON\", \"HR\", \"LS\"])\n )\n\n @staticmethod\n def schedule_activity_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for schedule activities.\"\"\"\n\n def dates_valid(record):\n start = record.get('start_date')\n end = record.get('end_date')\n if start and end:\n return start \u003c= end\n return True\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"id_required\", \"activity_id\")\n .add_required_rule(\"name_required\", \"activity_name\")\n .use_pattern(\"id_format\", \"activity_id\", \"activity_id\")\n .add_range_rule(\"duration_positive\", \"duration\", min_value=0)\n .add_range_rule(\"progress_range\", \"percent_complete\", min_value=0, max_value=100)\n )\n\n @staticmethod\n def bim_element_validator() -> ValidationRulesBuilder:\n \"\"\"Validator for BIM elements.\"\"\"\n\n return (ValidationRulesBuilder()\n .add_required_rule(\"guid_required\", \"element_guid\")\n .add_required_rule(\"type_required\", \"element_type\")\n .add_required_rule(\"level_required\", \"level\")\n .use_pattern(\"level_format\", \"level\", \"level_name\", severity=Severity.WARNING)\n .add_enum_rule(\"status_valid\", \"status\",\n [\"New\", \"Existing\", \"Demolished\", \"Temporary\"])\n )","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Quick Start","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"# Create validator\nvalidator = ValidationRulesBuilder()\n\n# Add rules\nvalidator.add_required_rule(\"id_required\", \"item_id\")\nvalidator.use_pattern(\"wbs_valid\", \"wbs_code\", \"wbs_code\")\nvalidator.add_range_rule(\"cost_range\", \"total_cost\", min_value=0, max_value=10000000)\nvalidator.add_enum_rule(\"status_valid\", \"status\", [\"Active\", \"Completed\", \"Cancelled\"])\n\n# Validate records\nrecords = [\n {\"item_id\": \"001\", \"wbs_code\": \"01.02.03\", \"total_cost\": 50000, \"status\": \"Active\"},\n {\"item_id\": \"\", \"wbs_code\": \"invalid\", \"total_cost\": -100, \"status\": \"Unknown\"}\n]\n\nresults = validator.validate_records(records)\nprint(f\"Valid: {results['valid_records']}/{results['total_records']}\")\nprint(f\"Errors: {results['error_count']}, Warnings: {results['warning_count']}\")","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Common Use Cases","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"1. Cost Data Validation","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"cost_validator = ConstructionValidators.cost_item_validator()\nresults = cost_validator.validate_records(cost_items)","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"2. Schedule Validation","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"schedule_validator = ConstructionValidators.schedule_activity_validator()\nresults = schedule_validator.validate_records(activities)","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"3. BIM Element Validation","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"bim_validator = ConstructionValidators.bim_element_validator()\nresults = bim_validator.validate_records(elements)","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Resources","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"DDC Book","type":"text","marks":[{"type":"strong"}]},{"text":": Chapter 2.6 - Data Quality Requirements","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Website","type":"text","marks":[{"type":"strong"}]},{"text":": https://datadrivenconstruction.io","type":"text"}]}]}]},{"type":"hr","attrs":{"markup":"---"}}]},"metadata":{"date":"2026-06-05","name":"validation-rules-builder","author":"@skillopedia","source":{"stars":155,"repo_name":"ddc_skills_for_ai_agents_in_construction","origin_url":"https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/blob/HEAD/2_DDC_Book/2.6-Data-Quality-Validation/validation-rules-builder/SKILL.md","repo_owner":"datadrivenconstruction","body_sha256":"b48331a2a5cf4211deb29821bfde8a003f88a49c2e1f2d2a5b54dbac675df080","cluster_key":"2b50e4942f4365bbaf10d92822bb3ab7a05d4720f7aa7f201835b16d9cd959cf","clean_bundle":{"format":"clean-skill-bundle-v1","source":"datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/2_DDC_Book/2.6-Data-Quality-Validation/validation-rules-builder/SKILL.md","attachments":[{"id":"50f9a3ed-53aa-5897-9ea5-189cc1e72737","key":"uploads/10433ee7-ad12-4ae0-b34e-97553e46c6c8/50f9a3ed-53aa-5897-9ea5-189cc1e72737/attachment.json","path":"claw.json","size":532,"sha256":"74318a3abff238143a14cd0dd6900d2fc81d30a51710186a4feffa90d6f31072","contentType":"application/json; charset=utf-8"},{"id":"dbac64e5-6917-5a7a-a84d-97cab3861cc6","key":"uploads/10433ee7-ad12-4ae0-b34e-97553e46c6c8/dbac64e5-6917-5a7a-a84d-97cab3861cc6/attachment.md","path":"instructions.md","size":1203,"sha256":"26dfc9e07418c837f369f9192da8a76434dce956ddc60eb9ef43a11f6abb608e","contentType":"text/markdown; charset=utf-8"}],"bundle_sha256":"1f932e5f5c8fcd91ea3f58ae83341c3e5c4c6d7b1014ce5915b8c53bc6d9daaf","attachment_count":2,"text_attachments":2,"attachment_storage":"skillopedia-attachments-v1","binary_attachments":0,"excluded_attachments":[]},"cluster_size":1,"skill_md_path":"2_DDC_Book/2.6-Data-Quality-Validation/validation-rules-builder/SKILL.md","import_metadata":{"date":"2026-06-05","author":"@skillopedia","version":"v1","category":"web-development","category_label":"Web"},"exact_dupes_collapsed_into_this":0},"version":"v1","category":"web-development","homepage":"https://datadrivenconstruction.io","metadata":{"openclaw":{"os":["darwin","linux","win32"],"emoji":"✔️","homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}},"import_tag":"clean-skills-v1","description":"Build validation rules for construction data. Create RegEx and logic-based validation for BIM elements, cost codes, and schedule data."}},"renderedAt":1782979483848}

Validation Rules Builder Business Case Problem Statement Construction data quality challenges: - Inconsistent naming conventions - Invalid cost codes and WBS - Missing or malformed data - Non-compliant BIM elements Solution Rule-based validation engine using RegEx and logic rules to ensure data quality across construction systems. Technical Implementation Quick Start Common Use Cases 1. Cost Data Validation 2. Schedule Validation 3. BIM Element Validation Resources - DDC Book : Chapter 2.6 - Data Quality Requirements - Website : https://datadrivenconstruction.io ---