Json Parser

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

Security Scan

The skill's requested capabilities and instructions align with its stated purpose (parsing/flattening construction JSON), but there are a few minor inconsistencies and missing dependency declarations you should check before installing.

Purpose & Capability

Name and description match the behavior in SKILL.md: parsing, validation, flattening, and conversion to DataFrames for construction/BIM/IoT JSON. Requesting python3 and filesystem access is reasonable for this task.

Instruction Scope

SKILL.md contains concrete Python code that reads files (parse_file) and expects user-provided file paths. Instructions.md explicitly tells the agent to gather input and process user-supplied files. This is within scope, but the claw.json 'filesystem' permission means the skill can access the agent filesystem—ensure policy/host limits prevent arbitrary file reads and that the agent only processes files the user authorizes.

Install Mechanism

This is an instruction-only skill (no install spec), so nothing is written to disk by the skill system. However, the provided Python code depends on pandas (imported as pd) but pandas is not declared in the skill requirements; ensure the runtime environment has pandas installed or the agent will fail when executing the code.

Credentials

The skill requests no environment variables or credentials. That is proportionate to its stated purpose. No unrelated secrets are requested.

Persistence & Privilege

always:false and normal autonomous invocation defaults are used. The skill does not request persistent or elevated privileges beyond filesystem access. Note: OS restriction is 'win32' but required binary is 'python3' which may not match common Windows python executables ('python'); this could cause runtime failures but is not a security issue by itself.

Assessment

This skill appears to do what it says (parse and flatten construction JSON), but before installing or running it: - Ensure the agent environment has Python and pandas available (the SKILL.md imports pandas but the skill doesn't declare that dependency). - Confirm the agent's filesystem permissions/restrictions: the skill will read files you provide, but a broad 'filesystem' permission could allow wider access—only provide explicit file paths and avoid giving system or credential files. - Note the win32 OS restriction and the required binary 'python3' may not match Windows setups (you may need to map/alias 'python3' to your Python interpreter). - There is a minor metadata/version mismatch in the manifest files (claw.json lists version 2.0.0 while registry shows 2.1.0); this is likely benign but worth verifying the source. If you need higher assurance, request a signed release or a package that declares/install dependencies (pandas) and a clear provenance for the homepage/source code.

JSON Parser for Construction Data

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")

Common Use Cases

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