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schema-validation模式验证

Agent Skill

schema-validation 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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367

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113

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118
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:schema-validation(模式验证)
来源仓库:https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
仓库路径:skills/schema-validation
安装命令:
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill schema-validation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill schema-validation

简介

schema-validation 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它可结合来源仓库、安装命令和原始 README 继续核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 当前顶部介绍为空,需参考原始 SKILL.md 获取详细功能说明。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Schema Validation for Construction Data

Overview

Validate data structures against defined schemas for construction data exchange. Ensure API payloads, file imports, and BIM exports conform to expected formats before processing.

Schema Validation Framework

Core Schema Validator

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
from enum import Enum
import json
import re
from datetime import datetime

class SchemaType(Enum):
    STRING = "string"
    NUMBER = "number"
    INTEGER = "integer"
    BOOLEAN = "boolean"
    ARRAY = "array"
    OBJECT = "object"
    DATE = "date"
    DATETIME = "datetime"
    CSI_CODE = "csi_code"
    CURRENCY = "currency"
    GUID = "guid"

@dataclass
class SchemaField:
    name: str
    type: SchemaType
    required: bool = True
    nullable: bool = False
    min_value: Optional[float] = None
    max_value: Optional[float] = None
    min_length: Optional[int] = None
    max_length: Optional[int] = None
    pattern: Optional[str] = None
    enum_values: Optional[List[Any]] = None
    items_schema: Optional['Schema'] = None  # For arrays
    properties: Optional[Dict[str, 'SchemaField']] = None  # For objects
    description: str = ""

@dataclass
class Schema:
    name: str
    version: str
    fields: Dict[str, SchemaField]
    description: str = ""

@dataclass
class SchemaValidationError:
    path: str
    message: str
    expected: str
    actual: Any

@dataclass
class SchemaValidationResult:
    is_valid: bool
    errors: List[SchemaValidationError] = field(default_factory=list)
    schema_name: str = ""
    schema_version: str = ""

    def add_error(self, path: str, message: str, expected: str, actual: Any):
        self.errors.append(SchemaValidationError(path, message, expected, actual))
        self.is_valid = False

    def to_report(self) -> str:
        lines = [
            f"Schema Validation: {self.schema_name} v{self.schema_version}",
            "=" * 50,
            f"Status: {'✓ VALID' if self.is_valid else '✗ INVALID'}",
            f"Errors: {len(self.errors)}",
            ""
        ]

        for error in self.errors:
            lines.append(f"❌ {error.path}")
            lines.append(f"   {error.message}")
            lines.append(f"   Expected: {error.expected}")
            lines.append(f"   Actual: {error.actual}")
            lines.append("")

        return "\n".join(lines)

class SchemaValidator:
    """Validate data against schemas."""

    # Custom type patterns
    PATTERNS = {
        SchemaType.CSI_CODE: r'^\d{2}\s?\d{2}\s?\d{2}$',
        SchemaType.GUID: r'^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$',
        SchemaType.CURRENCY: r'^-?\d+(\.\d{2})?$',
        SchemaType.DATE: r'^\d{4}-\d{2}-\d{2}$',
        SchemaType.DATETIME: r'^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}',
    }

    def validate(self, data: Any, schema: Schema) -> SchemaValidationResult:
        result = SchemaValidationResult(
            is_valid=True,
            schema_name=schema.name,
            schema_version=schema.version
        )

        self._validate_object(data, schema.fields, "", result)
        return result

    def _validate_object(self, data: Any, fields: Dict[str, SchemaField], path: str, result: SchemaValidationResult):
        if not isinstance(data, dict):
            result.add_error(path or "root", "Expected object", "object", type(data).__name__)
            return

        # Check required fields
        for field_name, field_schema in fields.items():
            field_path = f"{path}.{field_name}" if path else field_name

            if field_name not in data:
                if field_schema.required:
                    result.add_error(field_path, "Required field missing", "present", "missing")
                continue

            value = data[field_name]

            # Check nullable
            if value is None:
                if not field_schema.nullable:
                    result.add_error(field_path, "Field cannot be null", "non-null", "null")
                continue

            # Validate type
            self._validate_field(value, field_schema, field_path, result)

        # Check for extra fields (warning only)
        for key in data.keys():
            if key not in fields:
                # Could add warning here if needed
                pass

    def _validate_field(self, value: Any, schema: SchemaField, path: str, result: SchemaValidationResult):
        # Type validation
        if not self._check_type(value, schema.type):
            result.add_error(path, f"Invalid type", schema.type.value, type(value).__name__)
            return

        # String validations
        if schema.type == SchemaType.STRING:
            if schema.min_length and len(value) < schema.min_length:
                result.add_error(path, f"String too short", f"min {schema.min_length}", len(value))
            if schema.max_length and len(value) > schema.max_length:
                result.add_error(path, f"String too long", f"max {schema.max_length}", len(value))
            if schema.pattern and not re.match(schema.pattern, value):
                result.add_error(path, "Pattern mismatch", schema.pattern, value)

        # Numeric validations
        if schema.type in (SchemaType.NUMBER, SchemaType.INTEGER):
            if schema.min_value is not None and value < schema.min_value:
                result.add_error(path, "Value below minimum", f">= {schema.min_value}", value)
            if schema.max_value is not None and value > schema.max_value:
                result.add_error(path, "Value above maximum", f"<= {schema.max_value}", value)

        # Enum validation
        if schema.enum_values and value not in schema.enum_values:
            result.add_error(path, "Invalid enum value", str(schema.enum_values), value)

        # Array validation
        if schema.type == SchemaType.ARRAY and schema.items_schema:
            for i, item in enumerate(value):
                item_path = f"{path}[{i}]"
                if schema.items_schema.fields:
                    self._validate_object(item, schema.items_schema.fields, item_path, result)

        # Nested object validation
        if schema.type == SchemaType.OBJECT and schema.properties:
            self._validate_object(value, schema.properties, path, result)

        # Custom type validation
        if schema.type in self.PATTERNS:
            pattern = self.PATTERNS[schema.type]
            if not re.match(pattern, str(value)):
                result.add_error(path, f"Invalid {schema.type.value} format", pattern, value)

    def _check_type(self, value: Any, expected: SchemaType) -> bool:
        type_checks = {
            SchemaType.STRING: lambda v: isinstance(v, str),
            SchemaType.NUMBER: lambda v: isinstance(v, (int, float)),
            SchemaType.INTEGER: lambda v: isinstance(v, int) and not isinstance(v, bool),
            SchemaType.BOOLEAN: lambda v: isinstance(v, bool),
            SchemaType.ARRAY: lambda v: isinstance(v, list),
            SchemaType.OBJECT: lambda v: isinstance(v, dict),
            SchemaType.DATE: lambda v: isinstance(v, str),
            SchemaType.DATETIME: lambda v: isinstance(v, str),
            SchemaType.CSI_CODE: lambda v: isinstance(v, str),
            SchemaType.CURRENCY: lambda v: isinstance(v, (int, float, str)),
            SchemaType.GUID: lambda v: isinstance(v, str),
        }
        return type_checks.get(expected, lambda v: True)(value)

Construction Data Schemas

Cost Estimate Schema

# Define schema for cost estimate data
COST_ESTIMATE_SCHEMA = Schema(
    name="CostEstimate",
    version="1.0",
    description="Schema for construction cost estimates",
    fields={
        "project_id": SchemaField(
            name="project_id",
            type=SchemaType.STRING,
            required=True,
            description="Unique project identifier"
        ),
        "project_name": SchemaField(
            name="project_name",
            type=SchemaType.STRING,
            required=True,
            max_length=200
        ),
        "estimate_type": SchemaField(
            name="estimate_type",
            type=SchemaType.STRING,
            required=True,
            enum_values=["conceptual", "schematic", "design_development", "construction_documents", "bid"]
        ),
        "estimate_date": SchemaField(
            name="estimate_date",
            type=SchemaType.DATE,
            required=True
        ),
        "currency": SchemaField(
            name="currency",
            type=SchemaType.STRING,
            required=False,
            enum_values=["USD", "EUR", "GBP", "CAD"],
            nullable=True
        ),
        "gross_area": SchemaField(
            name="gross_area",
            type=SchemaType.NUMBER,
            required=True,
            min_value=0,
            description="Gross floor area in SF or SM"
        ),
        "line_items": SchemaField(
            name="line_items",
            type=SchemaType.ARRAY,
            required=True,
            items_schema=Schema(
                name="LineItem",
                version="1.0",
                fields={
                    "id": SchemaField(name="id", type=SchemaType.STRING, required=True),
                    "csi_code": SchemaField(name="csi_code", type=SchemaType.CSI_CODE, required=False, nullable=True),
                    "description": SchemaField(name="description", type=SchemaType.STRING, required=True, max_length=500),
                    "quantity": SchemaField(name="quantity", type=SchemaType.NUMBER, required=True, min_value=0),
                    "unit": SchemaField(name="unit", type=SchemaType.STRING, required=True),
                    "unit_cost": SchemaField(name="unit_cost", type=SchemaType.NUMBER, required=True, min_value=0),
                    "amount": SchemaField(name="amount", type=SchemaType.NUMBER, required=True, min_value=0),
                }
            )
        ),
        "subtotal": SchemaField(
            name="subtotal",
            type=SchemaType.NUMBER,
            required=True,
            min_value=0
        ),
        "contingency_percent": SchemaField(
            name="contingency_percent",
            type=SchemaType.NUMBER,
            required=False,
            min_value=0,
            max_value=50
        ),
        "total": SchemaField(
            name="total",
            type=SchemaType.NUMBER,
            required=True,
            min_value=0
        )
    }
)

Schedule Data Schema

SCHEDULE_SCHEMA = Schema(
    name="ProjectSchedule",
    version="1.0",
    description="Schema for project schedule data",
    fields={
        "project_id": SchemaField(name="project_id", type=SchemaType.STRING, required=True),
        "schedule_name": SchemaField(name="schedule_name", type=SchemaType.STRING, required=True),
        "data_date": SchemaField(name="data_date", type=SchemaType.DATE, required=True),
        "start_date": SchemaField(name="start_date", type=SchemaType.DATE, required=True),
        "finish_date": SchemaField(name="finish_date", type=SchemaType.DATE, required=True),
        "calendar": SchemaField(
            name="calendar",
            type=SchemaType.STRING,
            required=False,
            enum_values=["5-day", "6-day", "7-day"],
            nullable=True
        ),
        "tasks": SchemaField(
            name="tasks",
            type=SchemaType.ARRAY,
            required=True,
            items_schema=Schema(
                name="Task",
                version="1.0",
                fields={
                    "id": SchemaField(name="id", type=SchemaType.STRING, required=True),
                    "wbs": SchemaField(name="wbs", type=SchemaType.STRING, required=False, nullable=True),
                    "name": SchemaField(name="name", type=SchemaType.STRING, required=True, max_length=300),
                    "start_date": SchemaField(name="start_date", type=SchemaType.DATE, required=True),
                    "finish_date": SchemaField(name="finish_date", type=SchemaType.DATE, required=True),
                    "duration": SchemaField(name="duration", type=SchemaType.INTEGER, required=True, min_value=0),
                    "percent_complete": SchemaField(name="percent_complete", type=SchemaType.NUMBER, required=False, min_value=0, max_value=100),
                    "predecessors": SchemaField(name="predecessors", type=SchemaType.ARRAY, required=False, nullable=True),
                    "resources": SchemaField(name="resources", type=SchemaType.ARRAY, required=False, nullable=True),
                }
            )
        )
    }
)

BIM Element Schema

BIM_ELEMENT_SCHEMA = Schema(
    name="BIMElement",
    version="1.0",
    description="Schema for BIM element data",
    fields={
        "guid": SchemaField(name="guid", type=SchemaType.GUID, required=True),
        "ifc_class": SchemaField(
            name="ifc_class",
            type=SchemaType.STRING,
            required=True,
            pattern=r'^Ifc[A-Z][a-zA-Z]+$'
        ),
        "name": SchemaField(name="name", type=SchemaType.STRING, required=False, nullable=True),
        "description": SchemaField(name="description", type=SchemaType.STRING, required=False, nullable=True),
        "level": SchemaField(name="level", type=SchemaType.STRING, required=False, nullable=True),
        "classification": SchemaField(
            name="classification",
            type=SchemaType.OBJECT,
            required=False,
            nullable=True,
            properties={
                "system": SchemaField(name="system", type=SchemaType.STRING, required=True),
                "code": SchemaField(name="code", type=SchemaType.STRING, required=True),
                "name": SchemaField(name="name", type=SchemaType.STRING, required=False, nullable=True),
            }
        ),
        "quantities": SchemaField(
            name="quantities",
            type=SchemaType.OBJECT,
            required=False,
            nullable=True,
            properties={
                "area": SchemaField(name="area", type=SchemaType.NUMBER, required=False, min_value=0, nullable=True),
                "volume": SchemaField(name="volume", type=SchemaType.NUMBER, required=False, min_value=0, nullable=True),
                "length": SchemaField(name="length", type=SchemaType.NUMBER, required=False, min_value=0, nullable=True),
                "count": SchemaField(name="count", type=SchemaType.INTEGER, required=False, min_value=0, nullable=True),
            }
        ),
        "properties": SchemaField(name="properties", type=SchemaType.OBJECT, required=False, nullable=True)
    }
)

RFI Schema

RFI_SCHEMA = Schema(
    name="RFI",
    version="1.0",
    description="Schema for Request for Information",
    fields={
        "rfi_number": SchemaField(name="rfi_number", type=SchemaType.STRING, required=True, pattern=r'^RFI-\d+$'),
        "project_id": SchemaField(name="project_id", type=SchemaType.STRING, required=True),
        "subject": SchemaField(name="subject", type=SchemaType.STRING, required=True, max_length=500),
        "status": SchemaField(
            name="status",
            type=SchemaType.STRING,
            required=True,
            enum_values=["draft", "submitted", "in_review", "answered", "closed"]
        ),
        "priority": SchemaField(
            name="priority",
            type=SchemaType.STRING,
            required=False,
            enum_values=["low", "medium", "high", "critical"],
            nullable=True
        ),
        "date_submitted": SchemaField(name="date_submitted", type=SchemaType.DATE, required=True),
        "date_required": SchemaField(name="date_required", type=SchemaType.DATE, required=True),
        "from_company": SchemaField(name="from_company", type=SchemaType.STRING, required=True),
        "to_company": SchemaField(name="to_company", type=SchemaType.STRING, required=True),
        "spec_section": SchemaField(name="spec_section", type=SchemaType.CSI_CODE, required=False, nullable=True),
        "drawing_reference": SchemaField(name="drawing_reference", type=SchemaType.STRING, required=False, nullable=True),
        "question": SchemaField(name="question", type=SchemaType.STRING, required=True),
        "response": SchemaField(name="response", type=SchemaType.STRING, required=False, nullable=True),
        "date_responded": SchemaField(name="date_responded", type=SchemaType.DATE, required=False, nullable=True),
        "attachments": SchemaField(name="attachments", type=SchemaType.ARRAY, required=False, nullable=True)
    }
)

Schema Registry

class ConstructionSchemaRegistry:
    """Registry of construction data schemas."""

    def __init__(self):
        self.schemas: Dict[str, Schema] = {}
        self._register_defaults()

    def _register_defaults(self):
        self.register(COST_ESTIMATE_SCHEMA)
        self.register(SCHEDULE_SCHEMA)
        self.register(BIM_ELEMENT_SCHEMA)
        self.register(RFI_SCHEMA)

    def register(self, schema: Schema):
        key = f"{schema.name}:{schema.version}"
        self.schemas[key] = schema
        # Also register without version for latest
        self.schemas[schema.name] = schema

    def get(self, name: str, version: str = None) -> Optional[Schema]:
        if version:
            return self.schemas.get(f"{name}:{version}")
        return self.schemas.get(name)

    def validate(self, data: Any, schema_name: str, version: str = None) -> SchemaValidationResult:
        schema = self.get(schema_name, version)
        if not schema:
            result = SchemaValidationResult(is_valid=False)
            result.add_error("schema", f"Schema '{schema_name}' not found", "valid schema", "not found")
            return result

        validator = SchemaValidator()
        return validator.validate(data, schema)

    def list_schemas(self) -> List[str]:
        return [k for k in self.schemas.keys() if ':' in k]

Usage Examples

# Initialize registry
registry = ConstructionSchemaRegistry()

# Validate cost estimate
estimate_data = {
    "project_id": "PROJ-001",
    "project_name": "Downtown Office Tower",
    "estimate_type": "schematic",
    "estimate_date": "2026-01-15",
    "gross_area": 50000,
    "line_items": [
        {
            "id": "1",
            "csi_code": "03 30 00",
            "description": "Cast-in-place concrete",
            "quantity": 5000,
            "unit": "CY",
            "unit_cost": 150.00,
            "amount": 750000.00
        }
    ],
    "subtotal": 750000.00,
    "contingency_percent": 10,
    "total": 825000.00
}

result = registry.validate(estimate_data, "CostEstimate")
print(result.to_report())

# Validate RFI
rfi_data = {
    "rfi_number": "RFI-042",
    "project_id": "PROJ-001",
    "subject": "Concrete mix design clarification",
    "status": "submitted",
    "priority": "high",
    "date_submitted": "2026-01-20",
    "date_required": "2026-01-27",
    "from_company": "ABC Concrete",
    "to_company": "XYZ Architects",
    "spec_section": "03 30 00",
    "question": "Please clarify the required PSI for the foundation pour."
}

result = registry.validate(rfi_data, "RFI")
if result.is_valid:
    print("RFI data is valid")
else:
    print(result.to_report())

JSON Schema Export

def export_to_json_schema(schema: Schema) -> dict:
    """Export DDC schema to JSON Schema format."""

    def field_to_json_schema(field: SchemaField) -> dict:
        type_map = {
            SchemaType.STRING: "string",
            SchemaType.NUMBER: "number",
            SchemaType.INTEGER: "integer",
            SchemaType.BOOLEAN: "boolean",
            SchemaType.ARRAY: "array",
            SchemaType.OBJECT: "object",
            SchemaType.DATE: "string",
            SchemaType.DATETIME: "string",
            SchemaType.CSI_CODE: "string",
            SchemaType.CURRENCY: "number",
            SchemaType.GUID: "string",
        }

        js = {"type": type_map.get(field.type, "string")}

        if field.description:
            js["description"] = field.description
        if field.min_value is not None:
            js["minimum"] = field.min_value
        if field.max_value is not None:
            js["maximum"] = field.max_value
        if field.min_length is not None:
            js["minLength"] = field.min_length
        if field.max_length is not None:
            js["maxLength"] = field.max_length
        if field.pattern:
            js["pattern"] = field.pattern
        if field.enum_values:
            js["enum"] = field.enum_values

        if field.type == SchemaType.DATE:
            js["format"] = "date"
        if field.type == SchemaType.DATETIME:
            js["format"] = "date-time"
        if field.type == SchemaType.GUID:
            js["format"] = "uuid"

        return js

    properties = {}
    required = []

    for name, field in schema.fields.items():
        properties[name] = field_to_json_schema(field)
        if field.required:
            required.append(name)

    return {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "title": schema.name,
        "description": schema.description,
        "type": "object",
        "properties": properties,
        "required": required
    }

# Export to JSON Schema
json_schema = export_to_json_schema(COST_ESTIMATE_SCHEMA)
print(json.dumps(json_schema, indent=2))

Integration with DDC Pipeline

# Validate API request before processing
def validate_api_request(endpoint: str, payload: dict) -> SchemaValidationResult:
    schema_map = {
        '/api/estimates': 'CostEstimate',
        '/api/schedules': 'ProjectSchedule',
        '/api/rfis': 'RFI',
        '/api/bim/elements': 'BIMElement'
    }

    schema_name = schema_map.get(endpoint)
    if not schema_name:
        result = SchemaValidationResult(is_valid=True)
        return result

    registry = ConstructionSchemaRegistry()
    return registry.validate(payload, schema_name)

# Use in API handler
@app.post('/api/estimates')
def create_estimate(payload: dict):
    validation = validate_api_request('/api/estimates', payload)
    if not validation.is_valid:
        return {'error': 'Validation failed', 'details': [e.__dict__ for e in validation.errors]}, 400

    # Process valid data
    return process_estimate(payload)

Resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.95%
按下载量换算42

Claude

30.15%
按下载量换算36

Cursor

19.64%
按下载量换算23

Gemini CLI

9.8%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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