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 input-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/input-validation)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/input-validation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/input-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/input-validation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/input-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 | $0.04394 |
| Opus 5 | $0.00015 | $0.02197 |
| Sonnet 5 | $0.00006 | $0.00879 |
| Haiku 4.5 | $0.00003 | $0.00439 |
Grade A, and why
input-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:
- input-validation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Input Validation for Construction Data
Overview
Validate incoming construction data before processing to catch errors early. Domain-specific validation rules for estimates, schedules, BIM exports, and field data.
Validation Framework
Core Validator Class
from dataclasses import dataclass, field
from typing import List, Dict, Any, Callable, Optional
from enum import Enum
import re
from datetime import datetime
class ValidationSeverity(Enum):
ERROR = "error" # Must fix, blocks processing
WARNING = "warning" # Should review, allows processing
INFO = "info" # FYI, no action needed
@dataclass
class ValidationIssue:
field: str
message: str
severity: ValidationSeverity
value: Any = None
suggestion: str = None
@dataclass
class ValidationResult:
is_valid: bool
issues: List[ValidationIssue] = field(default_factory=list)
def add_error(self, field: str, message: str, value: Any = None, suggestion: str = None):
self.issues.append(ValidationIssue(field, message, ValidationSeverity.ERROR, value, suggestion))
self.is_valid = False
def add_warning(self, field: str, message: str, value: Any = None, suggestion: str = None):
self.issues.append(ValidationIssue(field, message, ValidationSeverity.WARNING, value, suggestion))
def add_info(self, field: str, message: str, value: Any = None):
self.issues.append(ValidationIssue(field, message, ValidationSeverity.INFO, value))
@property
def errors(self) -> List[ValidationIssue]:
return [i for i in self.issues if i.severity == ValidationSeverity.ERROR]
@property
def warnings(self) -> List[ValidationIssue]:
return [i for i in self.issues if i.severity == ValidationSeverity.WARNING]
def to_report(self) -> str:
lines = ["VALIDATION REPORT", "=" * 50]
lines.append(f"Status: {'PASSED' if self.is_valid else 'FAILED'}")
lines.append(f"Errors: {len(self.errors)}, Warnings: {len(self.warnings)}")
lines.append("")
for issue in self.issues:
icon = "❌" if issue.severity == ValidationSeverity.ERROR else "⚠️" if issue.severity == ValidationSeverity.WARNING else "ℹ️"
lines.append(f"{icon} [{issue.field}] {issue.message}")
if issue.suggestion:
lines.append(f" Suggestion: {issue.suggestion}")
return "\n".join(lines)
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 · 501 lines · 29 tokens per session scan A 166a54af3147
input-validation is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (312 stars, last pushed 21d ago), licensed MIT. It adds 29 tokens to every session and 4,394 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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verify
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phx-investigate
Investigate Elixir/Phoenix bugs root-cause first. Reproduce failures, cite evidence, and use optional Amp subagents only when useful.