Parse and validate JSON data from construction APIs, IoT sensors, and BIM exports. Transform nested JSON to flat DataFrames.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: claw.json, instructions.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.1.0

Provenance

Publisher: datadrivenconstruction
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 7 sections Open source page

Overview

Construction systems increasingly use JSON for data exchange - from IoT sensors to BIM metadata exports. This skill handles parsing, validation, and flattening of JSON structures.

Python Implementation

import json import pandas as pd from typing import Dict, Any, List, Optional, Union from dataclasses import dataclass from pathlib import Path @dataclass class JSONParseResult: """Result of JSON parsing operation.""" success: bool data: Any errors: List[str] record_count: int class ConstructionJSONParser: """Parse JSON data from construction sources.""" def __init__(self): self.errors: List[str] = [] def parse_file(self, file_path: str) -> JSONParseResult: """Parse JSON from file.""" try: with open(file_path, 'r', encoding='utf-8') as f: data = json.load(f) return JSONParseResult(True, data, [], self._count_records(data)) except json.JSONDecodeError as e: return JSONParseResult(False, None, [f"JSON Error: {e}"], 0) except Exception as e: return JSONParseResult(False, None, [str(e)], 0) def parse_string(self, json_string: str) -> JSONParseResult: """Parse JSON from string.""" try: data = json.loads(json_string) return JSONParseResult(True, data, [], self._count_records(data)) except json.JSONDecodeError as e: return JSONParseResult(False, None, [f"JSON Error: {e}"], 0) def _count_records(self, data: Any) -> int: """Count records in data.""" if isinstance(data, list): return len(data) elif isinstance(data, dict): return 1 return 0 def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]: """Flatten nested JSON to single-level dict.""" flat = {} for key, value in data.items(): new_key = f"{prefix}_{key}" if prefix else key if isinstance(value, dict): flat.update(self.flatten_json(value, new_key)) elif isinstance(value, list): if all(isinstance(i, (str, int, float, bool, type(None))) for i in value): flat[new_key] = value else: for i, item in enumerate(value): if isinstance(item, dict): flat.update(self.flatten_json(item, f"{new_key}_{i}")) else: flat[f"{new_key}_{i}"] = item else: flat[new_key] = value return flat def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame: """Convert JSON data to DataFrame.""" if isinstance(data, list): flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data] return pd.DataFrame(flat_records) elif isinstance(data, dict): if all(isinstance(v, list) for v in data.values()): # Dict of lists - columnar format return pd.DataFrame(data) else: flat = self.flatten_json(data) return pd.DataFrame([flat]) return pd.DataFrame() def extract_elements(self, data: Dict, path: str) -> List[Any]: """Extract elements using dot notation path.""" parts = path.split('.') current = data for part in parts: if isinstance(current, dict) and part in current: current = current[part] elif isinstance(current, list) and part.isdigit(): current = current[int(part)] else: return [] return current if isinstance(current, list) else [current] def validate_schema(self, data: Dict, required_fields: List[str]) -> Dict[str, Any]: """Validate JSON against required fields.""" flat = self.flatten_json(data) missing = [f for f in required_fields if f not in flat] present = [f for f in required_fields if f in flat] return { 'valid': len(missing) == 0, 'missing_fields': missing, 'present_fields': present, 'completeness': len(present) / len(required_fields) * 100 } # BIM JSON Parser class BIMJSONParser(ConstructionJSONParser): """Specialized parser for BIM JSON exports.""" def parse_bim_elements(self, data: Dict) -> pd.DataFrame: """Parse BIM elements from JSON export.""" elements = [] # Common BIM JSON structures if 'elements' in data: elements = data['elements'] elif 'objects' in data: elements = data['objects'] elif 'entities' in data: elements = data['entities'] elif isinstance(data, list): elements = data if not elements: return pd.DataFrame() # Flatten each element flat_elements = [] for elem in elements: if isinstance(elem, dict): flat = self.flatten_json(elem) flat_elements.append(flat) return pd.DataFrame(flat_elements) def extract_properties(self, element: Dict) -> Dict[str, Any]: """Extract properties from BIM element.""" props = {} # Common property locations in BIM JSON for key in ['properties', 'params', 'parameters', 'attributes']: if key in element and isinstance(element[key], dict): props.update(element[key]) return props # IoT JSON Parser class IoTJSONParser(ConstructionJSONParser): """Parser for IoT sensor data.""" def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]: """Parse single sensor reading.""" return { 'sensor_id': data.get('sensor_id') or data.get('id'), 'timestamp': data.get('timestamp') or data.get('time'), 'value': data.get('value') or data.get('reading'), 'unit': data.get('unit', ''), 'location': data.get('location', '') } def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame: """Parse batch of sensor readings.""" readings = [self.parse_sensor_reading(r) for r in data] return pd.DataFrame(readings)

Quick Start

parser = ConstructionJSONParser() # Parse from file result = parser.parse_file("bim_export.json") if result.success: df = parser.to_dataframe(result.data) print(f"Loaded {len(df)} records") # Flatten nested JSON flat = parser.flatten_json(result.data) # Extract specific path elements = parser.extract_elements(result.data, "project.building.floors")

1. BIM Metadata

bim_parser = BIMJSONParser() result = bim_parser.parse_file("revit_export.json") elements = bim_parser.parse_bim_elements(result.data)

2. IoT Sensors

iot_parser = IoTJSONParser() readings = iot_parser.parse_sensor_batch(sensor_data)

3. API Response

parser = ConstructionJSONParser() result = parser.parse_string(api_response) df = parser.to_dataframe(result.data)

Resources

DDC Book: Chapter 2.1 - Semi-structured Data

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs1 Config

SKILL.md Primary doc
instructions.md Docs
claw.json Config