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Agent Skill · 开发

schema-validation(模式验证)

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。

schema-validation 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 17,985。

总安装 367周安装 15GitHub Stars 113下载 118核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

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。

来源仓库内路径:skills/schema-validation

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agent…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

schema-validation 怎么安装?

在终端执行 npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_const…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

schema-validation 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

schema-validation 需要什么权限?

权限级别是需要联网。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。

schema-validation 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

schema-validation 用的人多吗?

公开数据是累计安装 367 次,近一周安装 15 次,下载 118 次,GitHub 113 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

schema-validation 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
schema-validation

模式验证 · 本页

367113需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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Codex

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Gemini CLI

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安全审计

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通过

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通过

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通过

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安装前确认

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