{ "schemaVersion": "1.0", "item": { "slug": "json-parser", "name": "Json Parser", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/datadrivenconstruction/json-parser", "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/json-parser", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/json-parser", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=json-parser", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "claw.json", "instructions.md", "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "slug": "json-parser", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-04T14:53:49.802Z", "expiresAt": "2026-05-11T14:53:49.802Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=json-parser", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=json-parser", "contentDisposition": "attachment; filename=\"json-parser-2.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "json-parser" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/json-parser" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/json-parser", "agentPageUrl": "https://openagent3.xyz/skills/json-parser/agent", "manifestUrl": "https://openagent3.xyz/skills/json-parser/agent.json", "briefUrl": "https://openagent3.xyz/skills/json-parser/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Overview", "body": "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." }, { "title": "Python Implementation", "body": "import json\nimport pandas as pd\nfrom typing import Dict, Any, List, Optional, Union\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\n\n@dataclass\nclass JSONParseResult:\n \"\"\"Result of JSON parsing operation.\"\"\"\n success: bool\n data: Any\n errors: List[str]\n record_count: int\n\n\nclass ConstructionJSONParser:\n \"\"\"Parse JSON data from construction sources.\"\"\"\n\n def __init__(self):\n self.errors: List[str] = []\n\n def parse_file(self, file_path: str) -> JSONParseResult:\n \"\"\"Parse JSON from file.\"\"\"\n try:\n with open(file_path, 'r', encoding='utf-8') as f:\n data = json.load(f)\n return JSONParseResult(True, data, [], self._count_records(data))\n except json.JSONDecodeError as e:\n return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\n except Exception as e:\n return JSONParseResult(False, None, [str(e)], 0)\n\n def parse_string(self, json_string: str) -> JSONParseResult:\n \"\"\"Parse JSON from string.\"\"\"\n try:\n data = json.loads(json_string)\n return JSONParseResult(True, data, [], self._count_records(data))\n except json.JSONDecodeError as e:\n return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\n\n def _count_records(self, data: Any) -> int:\n \"\"\"Count records in data.\"\"\"\n if isinstance(data, list):\n return len(data)\n elif isinstance(data, dict):\n return 1\n return 0\n\n def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]:\n \"\"\"Flatten nested JSON to single-level dict.\"\"\"\n flat = {}\n for key, value in data.items():\n new_key = f\"{prefix}_{key}\" if prefix else key\n\n if isinstance(value, dict):\n flat.update(self.flatten_json(value, new_key))\n elif isinstance(value, list):\n if all(isinstance(i, (str, int, float, bool, type(None))) for i in value):\n flat[new_key] = value\n else:\n for i, item in enumerate(value):\n if isinstance(item, dict):\n flat.update(self.flatten_json(item, f\"{new_key}_{i}\"))\n else:\n flat[f\"{new_key}_{i}\"] = item\n else:\n flat[new_key] = value\n return flat\n\n def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame:\n \"\"\"Convert JSON data to DataFrame.\"\"\"\n if isinstance(data, list):\n flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data]\n return pd.DataFrame(flat_records)\n elif isinstance(data, dict):\n if all(isinstance(v, list) for v in data.values()):\n # Dict of lists - columnar format\n return pd.DataFrame(data)\n else:\n flat = self.flatten_json(data)\n return pd.DataFrame([flat])\n return pd.DataFrame()\n\n def extract_elements(self, data: Dict, path: str) -> List[Any]:\n \"\"\"Extract elements using dot notation path.\"\"\"\n parts = path.split('.')\n current = data\n\n for part in parts:\n if isinstance(current, dict) and part in current:\n current = current[part]\n elif isinstance(current, list) and part.isdigit():\n current = current[int(part)]\n else:\n return []\n\n return current if isinstance(current, list) else [current]\n\n def validate_schema(self, data: Dict,\n required_fields: List[str]) -> Dict[str, Any]:\n \"\"\"Validate JSON against required fields.\"\"\"\n flat = self.flatten_json(data)\n missing = [f for f in required_fields if f not in flat]\n present = [f for f in required_fields if f in flat]\n\n return {\n 'valid': len(missing) == 0,\n 'missing_fields': missing,\n 'present_fields': present,\n 'completeness': len(present) / len(required_fields) * 100\n }\n\n\n# BIM JSON Parser\nclass BIMJSONParser(ConstructionJSONParser):\n \"\"\"Specialized parser for BIM JSON exports.\"\"\"\n\n def parse_bim_elements(self, data: Dict) -> pd.DataFrame:\n \"\"\"Parse BIM elements from JSON export.\"\"\"\n elements = []\n\n # Common BIM JSON structures\n if 'elements' in data:\n elements = data['elements']\n elif 'objects' in data:\n elements = data['objects']\n elif 'entities' in data:\n elements = data['entities']\n elif isinstance(data, list):\n elements = data\n\n if not elements:\n return pd.DataFrame()\n\n # Flatten each element\n flat_elements = []\n for elem in elements:\n if isinstance(elem, dict):\n flat = self.flatten_json(elem)\n flat_elements.append(flat)\n\n return pd.DataFrame(flat_elements)\n\n def extract_properties(self, element: Dict) -> Dict[str, Any]:\n \"\"\"Extract properties from BIM element.\"\"\"\n props = {}\n\n # Common property locations in BIM JSON\n for key in ['properties', 'params', 'parameters', 'attributes']:\n if key in element and isinstance(element[key], dict):\n props.update(element[key])\n\n return props\n\n\n# IoT JSON Parser\nclass IoTJSONParser(ConstructionJSONParser):\n \"\"\"Parser for IoT sensor data.\"\"\"\n\n def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]:\n \"\"\"Parse single sensor reading.\"\"\"\n return {\n 'sensor_id': data.get('sensor_id') or data.get('id'),\n 'timestamp': data.get('timestamp') or data.get('time'),\n 'value': data.get('value') or data.get('reading'),\n 'unit': data.get('unit', ''),\n 'location': data.get('location', '')\n }\n\n def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame:\n \"\"\"Parse batch of sensor readings.\"\"\"\n readings = [self.parse_sensor_reading(r) for r in data]\n return pd.DataFrame(readings)" }, { "title": "Quick Start", "body": "parser = ConstructionJSONParser()\n\n# Parse from file\nresult = parser.parse_file(\"bim_export.json\")\nif result.success:\n df = parser.to_dataframe(result.data)\n print(f\"Loaded {len(df)} records\")\n\n# Flatten nested JSON\nflat = parser.flatten_json(result.data)\n\n# Extract specific path\nelements = parser.extract_elements(result.data, \"project.building.floors\")" }, { "title": "1. BIM Metadata", "body": "bim_parser = BIMJSONParser()\nresult = bim_parser.parse_file(\"revit_export.json\")\nelements = bim_parser.parse_bim_elements(result.data)" }, { "title": "2. IoT Sensors", "body": "iot_parser = IoTJSONParser()\nreadings = iot_parser.parse_sensor_batch(sensor_data)" }, { "title": "3. API Response", "body": "parser = ConstructionJSONParser()\nresult = parser.parse_string(api_response)\ndf = parser.to_dataframe(result.data)" }, { "title": "Resources", "body": "DDC Book: Chapter 2.1 - Semi-structured Data" } ], "body": "JSON Parser for Construction Data\nOverview\n\nConstruction systems increasingly use JSON for data exchange - from IoT sensors to BIM metadata exports. This skill handles parsing, validation, and flattening of JSON structures.\n\nPython Implementation\nimport json\nimport pandas as pd\nfrom typing import Dict, Any, List, Optional, Union\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\n\n@dataclass\nclass JSONParseResult:\n \"\"\"Result of JSON parsing operation.\"\"\"\n success: bool\n data: Any\n errors: List[str]\n record_count: int\n\n\nclass ConstructionJSONParser:\n \"\"\"Parse JSON data from construction sources.\"\"\"\n\n def __init__(self):\n self.errors: List[str] = []\n\n def parse_file(self, file_path: str) -> JSONParseResult:\n \"\"\"Parse JSON from file.\"\"\"\n try:\n with open(file_path, 'r', encoding='utf-8') as f:\n data = json.load(f)\n return JSONParseResult(True, data, [], self._count_records(data))\n except json.JSONDecodeError as e:\n return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\n except Exception as e:\n return JSONParseResult(False, None, [str(e)], 0)\n\n def parse_string(self, json_string: str) -> JSONParseResult:\n \"\"\"Parse JSON from string.\"\"\"\n try:\n data = json.loads(json_string)\n return JSONParseResult(True, data, [], self._count_records(data))\n except json.JSONDecodeError as e:\n return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\n\n def _count_records(self, data: Any) -> int:\n \"\"\"Count records in data.\"\"\"\n if isinstance(data, list):\n return len(data)\n elif isinstance(data, dict):\n return 1\n return 0\n\n def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]:\n \"\"\"Flatten nested JSON to single-level dict.\"\"\"\n flat = {}\n for key, value in data.items():\n new_key = f\"{prefix}_{key}\" if prefix else key\n\n if isinstance(value, dict):\n flat.update(self.flatten_json(value, new_key))\n elif isinstance(value, list):\n if all(isinstance(i, (str, int, float, bool, type(None))) for i in value):\n flat[new_key] = value\n else:\n for i, item in enumerate(value):\n if isinstance(item, dict):\n flat.update(self.flatten_json(item, f\"{new_key}_{i}\"))\n else:\n flat[f\"{new_key}_{i}\"] = item\n else:\n flat[new_key] = value\n return flat\n\n def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame:\n \"\"\"Convert JSON data to DataFrame.\"\"\"\n if isinstance(data, list):\n flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data]\n return pd.DataFrame(flat_records)\n elif isinstance(data, dict):\n if all(isinstance(v, list) for v in data.values()):\n # Dict of lists - columnar format\n return pd.DataFrame(data)\n else:\n flat = self.flatten_json(data)\n return pd.DataFrame([flat])\n return pd.DataFrame()\n\n def extract_elements(self, data: Dict, path: str) -> List[Any]:\n \"\"\"Extract elements using dot notation path.\"\"\"\n parts = path.split('.')\n current = data\n\n for part in parts:\n if isinstance(current, dict) and part in current:\n current = current[part]\n elif isinstance(current, list) and part.isdigit():\n current = current[int(part)]\n else:\n return []\n\n return current if isinstance(current, list) else [current]\n\n def validate_schema(self, data: Dict,\n required_fields: List[str]) -> Dict[str, Any]:\n \"\"\"Validate JSON against required fields.\"\"\"\n flat = self.flatten_json(data)\n missing = [f for f in required_fields if f not in flat]\n present = [f for f in required_fields if f in flat]\n\n return {\n 'valid': len(missing) == 0,\n 'missing_fields': missing,\n 'present_fields': present,\n 'completeness': len(present) / len(required_fields) * 100\n }\n\n\n# BIM JSON Parser\nclass BIMJSONParser(ConstructionJSONParser):\n \"\"\"Specialized parser for BIM JSON exports.\"\"\"\n\n def parse_bim_elements(self, data: Dict) -> pd.DataFrame:\n \"\"\"Parse BIM elements from JSON export.\"\"\"\n elements = []\n\n # Common BIM JSON structures\n if 'elements' in data:\n elements = data['elements']\n elif 'objects' in data:\n elements = data['objects']\n elif 'entities' in data:\n elements = data['entities']\n elif isinstance(data, list):\n elements = data\n\n if not elements:\n return pd.DataFrame()\n\n # Flatten each element\n flat_elements = []\n for elem in elements:\n if isinstance(elem, dict):\n flat = self.flatten_json(elem)\n flat_elements.append(flat)\n\n return pd.DataFrame(flat_elements)\n\n def extract_properties(self, element: Dict) -> Dict[str, Any]:\n \"\"\"Extract properties from BIM element.\"\"\"\n props = {}\n\n # Common property locations in BIM JSON\n for key in ['properties', 'params', 'parameters', 'attributes']:\n if key in element and isinstance(element[key], dict):\n props.update(element[key])\n\n return props\n\n\n# IoT JSON Parser\nclass IoTJSONParser(ConstructionJSONParser):\n \"\"\"Parser for IoT sensor data.\"\"\"\n\n def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]:\n \"\"\"Parse single sensor reading.\"\"\"\n return {\n 'sensor_id': data.get('sensor_id') or data.get('id'),\n 'timestamp': data.get('timestamp') or data.get('time'),\n 'value': data.get('value') or data.get('reading'),\n 'unit': data.get('unit', ''),\n 'location': data.get('location', '')\n }\n\n def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame:\n \"\"\"Parse batch of sensor readings.\"\"\"\n readings = [self.parse_sensor_reading(r) for r in data]\n return pd.DataFrame(readings)\n\nQuick Start\nparser = ConstructionJSONParser()\n\n# Parse from file\nresult = parser.parse_file(\"bim_export.json\")\nif result.success:\n df = parser.to_dataframe(result.data)\n print(f\"Loaded {len(df)} records\")\n\n# Flatten nested JSON\nflat = parser.flatten_json(result.data)\n\n# Extract specific path\nelements = parser.extract_elements(result.data, \"project.building.floors\")\n\nCommon Use Cases\n1. BIM Metadata\nbim_parser = BIMJSONParser()\nresult = bim_parser.parse_file(\"revit_export.json\")\nelements = bim_parser.parse_bim_elements(result.data)\n\n2. IoT Sensors\niot_parser = IoTJSONParser()\nreadings = iot_parser.parse_sensor_batch(sensor_data)\n\n3. API Response\nparser = ConstructionJSONParser()\nresult = parser.parse_string(api_response)\ndf = parser.to_dataframe(result.data)\n\nResources\nDDC Book: Chapter 2.1 - Semi-structured Data" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/datadrivenconstruction/json-parser", "publisherUrl": "https://clawhub.ai/datadrivenconstruction/json-parser", "owner": "datadrivenconstruction", "version": "2.1.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/json-parser", "downloadUrl": "https://openagent3.xyz/downloads/json-parser", "agentUrl": "https://openagent3.xyz/skills/json-parser/agent", "manifestUrl": "https://openagent3.xyz/skills/json-parser/agent.json", "briefUrl": "https://openagent3.xyz/skills/json-parser/agent.md" } }