schema-validation

schema-validation is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 29 tokens per session (5,212 once invoked), scanned A, original, MIT.

A tool for checking whether construction data follows a defined JSON or file schema. A schema is a set of rules describing which fields data must contain and what type each value should have.

In plain words
What is it for?
Use it to validate API payloads, imported files, and BIM exports against expected data structures.
Why use it?
It catches missing fields, invalid value types, and incorrectly structured data before an API, file import, or building-model exchange processes it.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to validate API payloads, imported files, and BIM exports against expected data structures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill schema-validation
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for schema-validation

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for schema-validation

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,212 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.05212
Opus 5 $0.00015 $0.02606
Sonnet 5 $0.00006 $0.01042
Haiku 4.5 $0.00003 $0.00521

Measured 9d ago against content hash ce880dbeb6ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

schema-validation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

4_DDC_Curated/Data-Validation/schema-validation/SKILL.md · 628 lines

How it starts

The opening of the file, as written. The whole thing — 628 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

Read the full file on GitHub · 628 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 628 lines · 29 tokens per session scan A ce880dbeb6ab

Subscribe to this mod's changes

schema-validation is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 29 tokens to every session and 5,212 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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