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.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill schema-validationgit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schema-validation)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- schema-validation — 100% identical, 0 lines differ
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)
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.
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.
- 9d ago First seen · 628 lines · 29 tokens per session scan A ce880dbeb6ab
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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