[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FJZ36CSXFFBM98MTTPX5WG":3,"skill-same:01M6FJZ36CSXFFBM98MTTPX5WG":60},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":39,"install":40,"resources":53},"01M6FJZ36CSXFFBM98MTTPX5WG","json-parser",null,"Parse and validate JSON data from construction APIs, IoT sensors, and BIM exports. Transform nested JSON to flat DataFrames.","kbarbel640-del\u002Fskills","kbarbel640-del","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F255315490?v=4","01M6CJPPQGCCESXMG3K7BBGQ5J","01M6CJPPX2PB2BCZERFVM2C2B3",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fjson-parser\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26],"json","parser","json parser","validate","iot","data","nested","dataframes","transform","2026-11-10 10:12:30",false,"---\r\nslug: \"json-parser\"\r\ndisplay_name: \"Json Parser\"\r\ndescription: \"Parse and validate JSON data from construction APIs, IoT sensors, and BIM exports. Transform nested JSON to flat DataFrames.\"\r\n---\r\n\r\n# JSON Parser for Construction Data\r\n\r\n## Overview\r\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.\r\n\r\n## Python Implementation\r\n\r\n```python\r\nimport json\r\nimport pandas as pd\r\nfrom typing import Dict, Any, List, Optional, Union\r\nfrom dataclasses import dataclass\r\nfrom pathlib import Path\r\n\r\n\r\n@dataclass\r\nclass JSONParseResult:\r\n    \"\"\"Result of JSON parsing operation.\"\"\"\r\n    success: bool\r\n    data: Any\r\n    errors: List[str]\r\n    record_count: int\r\n\r\n\r\nclass ConstructionJSONParser:\r\n    \"\"\"Parse JSON data from construction sources.\"\"\"\r\n\r\n    def __init__(self):\r\n        self.errors: List[str] = []\r\n\r\n    def parse_file(self, file_path: str) -> JSONParseResult:\r\n        \"\"\"Parse JSON from file.\"\"\"\r\n        try:\r\n            with open(file_path, 'r', encoding='utf-8') as f:\r\n                data = json.load(f)\r\n            return JSONParseResult(True, data, [], self._count_records(data))\r\n        except json.JSONDecodeError as e:\r\n            return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\r\n        except Exception as e:\r\n            return JSONParseResult(False, None, [str(e)], 0)\r\n\r\n    def parse_string(self, json_string: str) -> JSONParseResult:\r\n        \"\"\"Parse JSON from string.\"\"\"\r\n        try:\r\n            data = json.loads(json_string)\r\n            return JSONParseResult(True, data, [], self._count_records(data))\r\n        except json.JSONDecodeError as e:\r\n            return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\r\n\r\n    def _count_records(self, data: Any) -> int:\r\n        \"\"\"Count records in data.\"\"\"\r\n        if isinstance(data, list):\r\n            return len(data)\r\n        elif isinstance(data, dict):\r\n            return 1\r\n        return 0\r\n\r\n    def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]:\r\n        \"\"\"Flatten nested JSON to single-level dict.\"\"\"\r\n        flat = {}\r\n        for key, value in data.items():\r\n            new_key = f\"{prefix}_{key}\" if prefix else key\r\n\r\n            if isinstance(value, dict):\r\n                flat.update(self.flatten_json(value, new_key))\r\n            elif isinstance(value, list):\r\n                if all(isinstance(i, (str, int, float, bool, type(None))) for i in value):\r\n                    flat[new_key] = value\r\n                else:\r\n                    for i, item in enumerate(value):\r\n                        if isinstance(item, dict):\r\n                            flat.update(self.flatten_json(item, f\"{new_key}_{i}\"))\r\n                        else:\r\n                            flat[f\"{new_key}_{i}\"] = item\r\n            else:\r\n                flat[new_key] = value\r\n        return flat\r\n\r\n    def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame:\r\n        \"\"\"Convert JSON data to DataFrame.\"\"\"\r\n        if isinstance(data, list):\r\n            flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data]\r\n            return pd.DataFrame(flat_records)\r\n        elif isinstance(data, dict):\r\n            if all(isinstance(v, list) for v in data.values()):\r\n                # Dict of lists - columnar format\r\n                return pd.DataFrame(data)\r\n            else:\r\n                flat = self.flatten_json(data)\r\n                return pd.DataFrame([flat])\r\n        return pd.DataFrame()\r\n\r\n    def extract_elements(self, data: Dict, path: str) -> List[Any]:\r\n        \"\"\"Extract elements using dot notation path.\"\"\"\r\n        parts = path.split('.')\r\n        current = data\r\n\r\n        for part in parts:\r\n            if isinstance(current, dict) and part in current:\r\n                current = current[part]\r\n            elif isinstance(current, list) and part.isdigit():\r\n                current = current[int(part)]\r\n            else:\r\n                return []\r\n\r\n        return current if isinstance(current, list) else [current]\r\n\r\n    def validate_schema(self, data: Dict,\r\n                        required_fields: List[str]) -> Dict[str, Any]:\r\n        \"\"\"Validate JSON against required fields.\"\"\"\r\n        flat = self.flatten_json(data)\r\n        missing = [f for f in required_fields if f not in flat]\r\n        present = [f for f in required_fields if f in flat]\r\n\r\n        return {\r\n            'valid': len(missing) == 0,\r\n            'missing_fields': missing,\r\n            'present_fields': present,\r\n            'completeness': len(present) \u002F len(required_fields) * 100\r\n        }\r\n\r\n\r\n# BIM JSON Parser\r\nclass BIMJSONParser(ConstructionJSONParser):\r\n    \"\"\"Specialized parser for BIM JSON exports.\"\"\"\r\n\r\n    def parse_bim_elements(self, data: Dict) -> pd.DataFrame:\r\n        \"\"\"Parse BIM elements from JSON export.\"\"\"\r\n        elements = []\r\n\r\n        # Common BIM JSON structures\r\n        if 'elements' in data:\r\n            elements = data['elements']\r\n        elif 'objects' in data:\r\n            elements = data['objects']\r\n        elif 'entities' in data:\r\n            elements = data['entities']\r\n        elif isinstance(data, list):\r\n            elements = data\r\n\r\n        if not elements:\r\n            return pd.DataFrame()\r\n\r\n        # Flatten each element\r\n        flat_elements = []\r\n        for elem in elements:\r\n            if isinstance(elem, dict):\r\n                flat = self.flatten_json(elem)\r\n                flat_elements.append(flat)\r\n\r\n        return pd.DataFrame(flat_elements)\r\n\r\n    def extract_properties(self, element: Dict) -> Dict[str, Any]:\r\n        \"\"\"Extract properties from BIM element.\"\"\"\r\n        props = {}\r\n\r\n        # Common property locations in BIM JSON\r\n        for key in ['properties', 'params', 'parameters', 'attributes']:\r\n            if key in element and isinstance(element[key], dict):\r\n                props.update(element[key])\r\n\r\n        return props\r\n\r\n\r\n# IoT JSON Parser\r\nclass IoTJSONParser(ConstructionJSONParser):\r\n    \"\"\"Parser for IoT sensor data.\"\"\"\r\n\r\n    def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]:\r\n        \"\"\"Parse single sensor reading.\"\"\"\r\n        return {\r\n            'sensor_id': data.get('sensor_id') or data.get('id'),\r\n            'timestamp': data.get('timestamp') or data.get('time'),\r\n            'value': data.get('value') or data.get('reading'),\r\n            'unit': data.get('unit', ''),\r\n            'location': data.get('location', '')\r\n        }\r\n\r\n    def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame:\r\n        \"\"\"Parse batch of sensor readings.\"\"\"\r\n        readings = [self.parse_sensor_reading(r) for r in data]\r\n        return pd.DataFrame(readings)\r\n```\r\n\r\n## Quick Start\r\n\r\n```python\r\nparser = ConstructionJSONParser()\r\n\r\n# Parse from file\r\nresult = parser.parse_file(\"bim_export.json\")\r\nif result.success:\r\n    df = parser.to_dataframe(result.data)\r\n    print(f\"Loaded {len(df)} records\")\r\n\r\n# Flatten nested JSON\r\nflat = parser.flatten_json(result.data)\r\n\r\n# Extract specific path\r\nelements = parser.extract_elements(result.data, \"project.building.floors\")\r\n```\r\n\r\n## Common Use Cases\r\n\r\n### 1. BIM Metadata\r\n```python\r\nbim_parser = BIMJSONParser()\r\nresult = bim_parser.parse_file(\"revit_export.json\")\r\nelements = bim_parser.parse_bim_elements(result.data)\r\n```\r\n\r\n### 2. IoT Sensors\r\n```python\r\niot_parser = IoTJSONParser()\r\nreadings = iot_parser.parse_sensor_batch(sensor_data)\r\n```\r\n\r\n### 3. API Response\r\n```python\r\nparser = ConstructionJSONParser()\r\nresult = parser.parse_string(api_response)\r\ndf = parser.to_dataframe(result.data)\r\n```\r\n\r\n## Resources\r\n- **DDC Book**: Chapter 2.1 - Semi-structured Data\r\n","# JSON Parser for Construction Data\r\n\r\n## Overview\r\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.\r\n\r\n## Python Implementation\r\n\r\n```python\r\nimport json\r\nimport pandas as pd\r\nfrom typing import Dict, Any, List, Optional, Union\r\nfrom dataclasses import dataclass\r\nfrom pathlib import Path\r\n\r\n\r\n@dataclass\r\nclass JSONParseResult:\r\n    \"\"\"Result of JSON parsing operation.\"\"\"\r\n    success: bool\r\n    data: Any\r\n    errors: List[str]\r\n    record_count: int\r\n\r\n\r\nclass ConstructionJSONParser:\r\n    \"\"\"Parse JSON data from construction sources.\"\"\"\r\n\r\n    def __init__(self):\r\n        self.errors: List[str] = []\r\n\r\n    def parse_file(self, file_path: str) -> JSONParseResult:\r\n        \"\"\"Parse JSON from file.\"\"\"\r\n        try:\r\n            with open(file_path, 'r', encoding='utf-8') as f:\r\n                data = json.load(f)\r\n            return JSONParseResult(True, data, [], self._count_records(data))\r\n        except json.JSONDecodeError as e:\r\n            return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\r\n        except Exception as e:\r\n            return JSONParseResult(False, None, [str(e)], 0)\r\n\r\n    def parse_string(self, json_string: str) -> JSONParseResult:\r\n        \"\"\"Parse JSON from string.\"\"\"\r\n        try:\r\n            data = json.loads(json_string)\r\n            return JSONParseResult(True, data, [], self._count_records(data))\r\n        except json.JSONDecodeError as e:\r\n            return JSONParseResult(False, None, [f\"JSON Error: {e}\"], 0)\r\n\r\n    def _count_records(self, data: Any) -> int:\r\n        \"\"\"Count records in data.\"\"\"\r\n        if isinstance(data, list):\r\n            return len(data)\r\n        elif isinstance(data, dict):\r\n            return 1\r\n        return 0\r\n\r\n    def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]:\r\n        \"\"\"Flatten nested JSON to single-level dict.\"\"\"\r\n        flat = {}\r\n        for key, value in data.items():\r\n            new_key = f\"{prefix}_{key}\" if prefix else key\r\n\r\n            if isinstance(value, dict):\r\n                flat.update(self.flatten_json(value, new_key))\r\n            elif isinstance(value, list):\r\n                if all(isinstance(i, (str, int, float, bool, type(None))) for i in value):\r\n                    flat[new_key] = value\r\n                else:\r\n                    for i, item in enumerate(value):\r\n                        if isinstance(item, dict):\r\n                            flat.update(self.flatten_json(item, f\"{new_key}_{i}\"))\r\n                        else:\r\n                            flat[f\"{new_key}_{i}\"] = item\r\n            else:\r\n                flat[new_key] = value\r\n        return flat\r\n\r\n    def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame:\r\n        \"\"\"Convert JSON data to DataFrame.\"\"\"\r\n        if isinstance(data, list):\r\n            flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data]\r\n            return pd.DataFrame(flat_records)\r\n        elif isinstance(data, dict):\r\n            if all(isinstance(v, list) for v in data.values()):\r\n                # Dict of lists - columnar format\r\n                return pd.DataFrame(data)\r\n            else:\r\n                flat = self.flatten_json(data)\r\n                return pd.DataFrame([flat])\r\n        return pd.DataFrame()\r\n\r\n    def extract_elements(self, data: Dict, path: str) -> List[Any]:\r\n        \"\"\"Extract elements using dot notation path.\"\"\"\r\n        parts = path.split('.')\r\n        current = data\r\n\r\n        for part in parts:\r\n            if isinstance(current, dict) and part in current:\r\n                current = current[part]\r\n            elif isinstance(current, list) and part.isdigit():\r\n                current = current[int(part)]\r\n            else:\r\n                return []\r\n\r\n        return current if isinstance(current, list) else [current]\r\n\r\n    def validate_schema(self, data: Dict,\r\n                        required_fields: List[str]) -> Dict[str, Any]:\r\n        \"\"\"Validate JSON against required fields.\"\"\"\r\n        flat = self.flatten_json(data)\r\n        missing = [f for f in required_fields if f not in flat]\r\n        present = [f for f in required_fields if f in flat]\r\n\r\n        return {\r\n            'valid': len(missing) == 0,\r\n            'missing_fields': missing,\r\n            'present_fields': present,\r\n            'completeness': len(present) \u002F len(required_fields) * 100\r\n        }\r\n\r\n\r\n# BIM JSON Parser\r\nclass BIMJSONParser(ConstructionJSONParser):\r\n    \"\"\"Specialized parser for BIM JSON exports.\"\"\"\r\n\r\n    def parse_bim_elements(self, data: Dict) -> pd.DataFrame:\r\n        \"\"\"Parse BIM elements from JSON export.\"\"\"\r\n        elements = []\r\n\r\n        # Common BIM JSON structures\r\n        if 'elements' in data:\r\n            elements = data['elements']\r\n        elif 'objects' in data:\r\n            elements = data['objects']\r\n        elif 'entities' in data:\r\n            elements = data['entities']\r\n        elif isinstance(data, list):\r\n            elements = data\r\n\r\n        if not elements:\r\n            return pd.DataFrame()\r\n\r\n        # Flatten each element\r\n        flat_elements = []\r\n        for elem in elements:\r\n            if isinstance(elem, dict):\r\n                flat = self.flatten_json(elem)\r\n                flat_elements.append(flat)\r\n\r\n        return pd.DataFrame(flat_elements)\r\n\r\n    def extract_properties(self, element: Dict) -> Dict[str, Any]:\r\n        \"\"\"Extract properties from BIM element.\"\"\"\r\n        props = {}\r\n\r\n        # Common property locations in BIM JSON\r\n        for key in ['properties', 'params', 'parameters', 'attributes']:\r\n            if key in element and isinstance(element[key], dict):\r\n                props.update(element[key])\r\n\r\n        return props\r\n\r\n\r\n# IoT JSON Parser\r\nclass IoTJSONParser(ConstructionJSONParser):\r\n    \"\"\"Parser for IoT sensor data.\"\"\"\r\n\r\n    def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]:\r\n        \"\"\"Parse single sensor reading.\"\"\"\r\n        return {\r\n            'sensor_id': data.get('sensor_id') or data.get('id'),\r\n            'timestamp': data.get('timestamp') or data.get('time'),\r\n            'value': data.get('value') or data.get('reading'),\r\n            'unit': data.get('unit', ''),\r\n            'location': data.get('location', '')\r\n        }\r\n\r\n    def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame:\r\n        \"\"\"Parse batch of sensor readings.\"\"\"\r\n        readings = [self.parse_sensor_reading(r) for r in data]\r\n        return pd.DataFrame(readings)\r\n```\r\n\r\n## Quick Start\r\n\r\n```python\r\nparser = ConstructionJSONParser()\r\n\r\n# Parse from file\r\nresult = parser.parse_file(\"bim_export.json\")\r\nif result.success:\r\n    df = parser.to_dataframe(result.data)\r\n    print(f\"Loaded {len(df)} records\")\r\n\r\n# Flatten nested JSON\r\nflat = parser.flatten_json(result.data)\r\n\r\n# Extract specific path\r\nelements = parser.extract_elements(result.data, \"project.building.floors\")\r\n```\r\n\r\n## Common Use Cases\r\n\r\n### 1. BIM Metadata\r\n```python\r\nbim_parser = BIMJSONParser()\r\nresult = bim_parser.parse_file(\"revit_export.json\")\r\nelements = bim_parser.parse_bim_elements(result.data)\r\n```\r\n\r\n### 2. IoT Sensors\r\n```python\r\niot_parser = IoTJSONParser()\r\nreadings = iot_parser.parse_sensor_batch(sensor_data)\r\n```\r\n\r\n### 3. API Response\r\n```python\r\nparser = ConstructionJSONParser()\r\nresult = parser.parse_string(api_response)\r\ndf = parser.to_dataframe(result.data)\r\n```\r\n\r\n## Resources\r\n- **DDC Book**: Chapter 2.1 - Semi-structured Data\r\n",{"slug":5,"description":7,"display_name":32},"Json Parser","main","e7e336ac628c3e3bd771aff1aaa469c93778f94a","skills\u002Fdatadrivenconstruction\u002Fjson-parser",0,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del","2026-08-23 22:39:37",{"source":8,"skillName":5,"commands":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add kbarbel640-del\u002Fskills --skill json-parser",{"tool":46,"command":47},"bunx","bunx skills add kbarbel640-del\u002Fskills --skill json-parser",{"tool":49,"command":50},"pnpm","pnpm dlx skills add kbarbel640-del\u002Fskills --skill json-parser","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fjson-parser\u002FSKILL.md",true,[54],{"path":55,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":58,"sourceHtmlUrl":59},"_meta.json","REFERENCE","参考文档",290,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdatadrivenconstruction\u002Fjson-parser\u002F_meta.json",[61,92,121,142,148,168],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":72,"publishedAt":27},"01M6G8J55967D6281ACMKAKTEQ","crowd-prompting","A marketplace where AI agents improve prompts, system instructions, tool descriptions, and other text-based content with domain expertise from real-world operations — and earn tokens for valuable contributions.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzzadrian\u002Fcrowd-prompting\u002FSKILL.md",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91],"crowd prompting","prompting","api","with","tokens","domain","world","prompts","operations","instructions","text","tool","system","improve","productivity","marketplace","https","content","ai agents",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":27},"01M6G8J0PKPAYVET6BWE8W4C26","dilemm-ai","Compete in DilemmAI, the prisoner's dilemma AI arena at dilemm.ai. Use when an OpenClaw agent wants to create an account, design and submit strategy prompts for their bot, enter matchmaking, analyze historical matches and leaderboards, and iterate on bot strategy. Requires headless browser automation (no API available yet).","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzyori\u002Fdilemm-ai\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[104,75,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,80,120],"dilemm ai","arena","analyze","browser","openclaw agent","create","automation","browser automation","bot","openclaw","design","strategy","headless","compete","submit","matchmaking","use",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":130,"publishedAt":27},"01M6G8HWAF3K9SW7NP3Z83CPJ6","arxiv-paper-reviews","Interact with arXiv Crawler API to fetch papers, read reviews, and submit comments. Use when working with arXiv papers, fetching paper lists by date\u002Fcategory\u002Finterest, viewing paper details with comments, or submitting paper reviews via API at http:\u002F\u002F150.158.152.82:8000.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Fweak-accept\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000021","资料检索与研究",[131,132,133,134,135,136,137,76,138,118,120,139,75,140,141],"arxiv paper reviews","reviews","arxiv","paper","crawler","papers","comments","fetch","read","http","via",{"skillNo":143,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":27},"01M6G8HQJ7N7WMY6YV7D7SSJ16","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzxrys\u002Farxiv-paper-reviews\u002FSKILL.md",[146],{"categoryNo":128,"categoryName":129,"taxonomyType":101,"taxonomyTypeDesc":102},[134,133,132,131,141,136,137,76,135,118,120,138,139,75,140],{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":27},"01M6G8HM95Y64JNVFC2HN4E2BG","git-summary","Get a quick summary of the current Git repository including status, recent commits, branches, and contributors.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzweack\u002Fgit-summary\u002FSKILL.md",[],[155,156,157,158,159,160,161,162,163,164,165,166,167],"git","summary","git summary","commits","repository","win32","status","linux","contributors","true","quick","get","branches",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":178,"publishedAt":27},"01M6G8HGEGF05CNEJCGVYFSC4R","agent-constitution","Interact with AgentConstitution governance contracts on Base Sepolia. Check compliance, read rules, log actions, query governance state.","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fztsalexey\u002Fagent-constitution\u002FSKILL.md",[174,175],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":176,"categoryName":177,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000037","律师与法务",[179,180,181,182,76,183,184,33,185,139,186,187,188,189,190,89,191,192,193],"agent constitution","constitution","contracts","tree","query","base","log","check","Rules","projects","compliance","actions","state","governance","github"]