standards-compliance-checker

standards-compliance-checker is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 31 tokens per session (2,508 once invoked), scanned A, original, MIT.

A checker for construction project data against standards such as ISO 19650, IFC, COBie, and UniFormat. These standards define common ways to organize and exchange building information.

In plain words
What is it for?
Use it to validate project data, identify compliance issues, and produce reports for standards-based quality checks.
Why use it?
It reduces manual checking across complex rules and highlights inconsistent or non-compliant data. The result is a report showing passed rules, failures, warnings, and issue severity.

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 project data, identify compliance issues, and produce reports for standards-based quality checks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/standards-compliance-checker
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 standards-compliance-checker
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 standards-compliance-checker

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/standards-compliance-checker/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/standards-compliance-checker)
Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/standards-compliance-checker"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/standards-compliance-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,508 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 $0.00031 $0.02508
Opus 5.5 $0.00012 $0.01003
Sonnet 5.5 $0.00006 $0.00502
Haiku 4.5 $0.00003 $0.00251

Measured 1mo ago against content hash 707cb744fe7c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

standards-compliance-checker 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 1mo 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:

2_DDC_Book/2.5-Data-Modeling-Standards/standards-compliance-checker/SKILL.md · 314 lines

How it starts

The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Standards Compliance Checker

Business Case

Problem Statement

Construction data compliance challenges:

  • Multiple standards to meet
  • Complex validation rules
  • Inconsistent implementations
  • Manual checking is error-prone

Solution

Automated compliance checking against major construction data standards including ISO 19650, IFC, COBie, and UniFormat.

Technical Implementation

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


class Standard(Enum):
    ISO_19650 = "iso_19650"
    IFC = "ifc"
    COBIE = "cobie"
    UNIFORMAT = "uniformat"
    OMNICLASS = "omniclass"
    MASTERFORMAT = "masterformat"


class ComplianceLevel(Enum):
    COMPLIANT = "compliant"
    MINOR_ISSUES = "minor_issues"
    MAJOR_ISSUES = "major_issues"
    NON_COMPLIANT = "non_compliant"


@dataclass
class ComplianceIssue:
    rule_id: str
    rule_name: str
    severity: str  # error, warning, info
    message: str
    field: str = ""
    value: Any = None


@dataclass
class ComplianceReport:
    standard: Standard
    total_rules: int
    passed: int
    failed: int
    warnings: int
    compliance_level: ComplianceLevel
    issues: List[ComplianceIssue] = field(default_factory=list)


class StandardsComplianceChecker:
    """Check compliance with construction data standards."""

    def __init__(self):
        self.rules: Dict[Standard, List[Dict]] = self._load_rules()

    def _load_rules(self) -> Dict[Standard, List[Dict]]:
        """Load compliance rules for each standard."""

        return {
            Standard.ISO_19650: [
                {"id": "ISO-001", "name": "File naming convention", "field": "filename",
                 "pattern": r"^[A-Z]{2,6}-[A-Z]{2,4}-[A-Z]{2,3}-[A-Z0-9]{2,4}-[A-Z]{2,3}-[A-Z]{2,4}-[A-Z0-9]{3,8}$"},
                {"id": "ISO-002", "name": "Status code valid", "field": "status",
                 "values": ["WIP", "S0", "S1", "S2", "S3", "S4", "A", "B", "CR"]},
                {"id": "ISO-003", "name": "Revision format", "field": "revision",
                 "pattern": r"^P[0-9]{2}|C[0-9]{2}$"},
            ],
            Standard.IFC: [
                {"id": "IFC-001", "name": "GUID format", "field": "global_id",
                 "pattern": r"^[0-9A-Za-z_$]{22}$"},
                {"id": "IFC-002", "name": "Name required", "field": "name", "required": True},
                {"id": "IFC-003", "name": "ObjectType defined", "field": "object_type", "required": True},
            ],
            Standard.COBIE: [
                {"id": "COB-001", "name": "Facility name", "field": "facility_name", "required": True},
                {"id": "COB-002", "name": "Space name format", "field": "space_name",
                 "pattern": r"^[A-Z0-9]{2,10}[-_]?[A-Z0-9]{0,10}$"},
                {"id": "COB-003", "name": "Component type", "field": "component_type", "required": True},
                {"id": "COB-004", "name": "Manufacturer info", "field": "manufacturer", "required": True},
            ],
            Standard.UNIFORMAT: [
                {"id": "UNI-001", "name": "Level 1 code", "field": "level1",
                 "values": ["A", "B", "C", "D", "E", "F", "G", "Z"]},
                {"id": "UNI-002", "name": "Code format", "field": "code",
                 "pattern": r"^[A-G][0-9]{4}$"},
            ],
            Standard.MASTERFORMAT: [
                {"id": "MF-001", "name": "Division format", "field": "division",
                 "pattern": r"^[0-9]{2}$"},
                {"id": "MF-002", "name": "Section format", "field": "section",
                 "pattern": r"^[0-9]{2}\s?[0-9]{2}\s?[0-9]{2}(\.[0-9]{2})?$"},
            ]
        }

    def check_compliance(self, data: Dict[str, Any],
                        standard: Standard) -> ComplianceReport:
        """Check data against specified standard."""

        rules = self.rules.get(standard, [])
        issues = []
        passed = 0
        failed = 0
        warnings = 0

        for rule in rules:
            result = self._check_rule(data, rule)
            if result:
                issues.append(result)
                if result.severity == "error":
                    failed += 1
                else:
                    warnings += 1
            else:
                passed += 1

        # Determine compliance level
        if failed == 0 and warnings == 0:
            level = ComplianceLevel.COMPLIANT
        elif failed == 0:
            level = ComplianceLevel.MINOR_ISSUES
        elif failed <= len(rules) * 0.3:
            level = ComplianceLevel.MAJOR_ISSUES
        else:
            level = ComplianceLevel.NON_COMPLIANT

        return ComplianceReport(
            standard=standard,
            total_rules=len(rules),
            passed=passed,
            failed=failed,
            warnings=warnings,
            compliance_level=level,
            issues=issues
        )

    def _check_rule(self, data: Dict[str, Any], rule: Dict) -> Optional[ComplianceIssue]:
        """Check single compliance rule."""

        field = rule.get('field', '')
        value = data.get(field)

        # Required check
        if rule.get('required') and (value is None or value == ''):
            return ComplianceIssue(
                rule_id=rule['id'],
                rule_name=rule['name'],
                severity="error",
                message=f"Required field '{field}' is missing",
                field=field
            )

        # Skip other checks if value is empty
        if value is None or value == '':
            return None

        # Pattern check
        if 'pattern' in rule:
            if not re.match(rule['pattern'], str(value)):
                return ComplianceIssue(
                    rule_id=rule['id'],
                    rule_name=rule['name'],
                    severity="error",
                    message=f"Field '{field}' does not match required format",
                    field=field,
                    value=value
                )

        # Allowed values check
        if 'values' in rule:
            if value not in rule['values']:
                return ComplianceIssue(
                    rule_id=rule['id'],
                    rule_name=rule['name'],
                    severity="error",
                    message=f"Field '{field}' must be one of: {rule['values']}",
                    field=field,
                    value=value
                )

        return None

    def check_multiple_standards(self, data: Dict[str, Any],
                                  standards: List[Standard]) -> Dict[str, ComplianceReport]:
        """Check data against multiple standards."""

        reports = {}
        for standard in standards:
            reports[standard.value] = self.check_compliance(data, standard)
        return reports

    def check_batch(self, records: List[Dict[str, Any]],
                    standard: Standard) -> Dict[str, Any]:
        """Check multiple records against standard."""

        all_issues = []
        compliant_count = 0

        for i, record in enumerate(records):
            report = self.check_compliance(record, standard)
            if report.compliance_level == ComplianceLevel.COMPLIANT:
                compliant_count += 1
            for issue in report.issues:
                all_issues.append({
                    'record_index': i,
                    'rule_id': issue.rule_id,
                    'field': issue.field,
                    'message': issue.message
                })

        return {
            'standard': standard.value,
            'total_records': len(records),
            'compliant_records': compliant_count,
            'compliance_rate': round(compliant_count / len(records) * 100, 1) if records else 0,
            'total_issues': len(all_issues),
            'issues': all_issues
        }

    def add_custom_rule(self, standard: Standard, rule: Dict):
        """Add custom compliance rule."""

        if standard not in self.rules:
            self.rules[standard] = []
        self.rules[standard].append(rule)

    def generate_report_summary(self, report: ComplianceReport) -> str:
        """Generate human-readable report summary."""

        lines = [
            f"Compliance Report: {report.standard.value.upper()}",
            "=" * 40,
            f"Total Rules: {report.total_rules}",
            f"Passed: {report.passed}",
            f"Failed: {report.failed}",
            f"Warnings: {report.warnings}",
            f"Status: {report.compliance_level.value.upper()}",
            "",
            "Issues:"
        ]

        for issue in report.issues:
            lines.append(f"  [{issue.severity.upper()}] {issue.rule_id}: {issue.message}")

        return "\n".join(lines)

Read the full file on GitHub · 314 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. 1mo ago First seen · 314 lines · 31 tokens per session scan A 707cb744fe7c

Subscribe to this mod's changes

standards-compliance-checker is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 31 tokens to every session and 2,508 once invoked, about $0.0001 per session on Opus 5.5. 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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