verification-loop-construction

verification-loop-construction is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 32 tokens per session (3,060 once invoked), scanned A, original, MIT.

A verification framework for checking construction estimates, schedules, reports, BIM/CAD data, and other automation outputs before delivery.

In plain words
What is it for?
Use it to check data completeness and consistency after estimates, schedules, reports, data pipelines, or document-processing tasks.
Why use it?
It helps find missing information and inconsistencies before results are submitted or used in a workflow.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; built for openclaw.

Good fit Use it to check data completeness and consistency after estimates, schedules, reports, data pipelines, or document-processing tasks.

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Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction
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 verification-loop-construction
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 verification-loop-construction

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction/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 verification-loop-construction

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/verification-loop-construction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,060 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 $0.00032 $0.03060
Opus 5 $0.00016 $0.01530
Sonnet 5 $0.00006 $0.00612
Haiku 4.5 $0.00003 $0.00306

Measured 9d ago against content hash e1813ea750c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

verification-loop-construction 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/Quality-Assurance/verification-loop-construction/SKILL.md · 437 lines

How it starts

The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Verification Loop for Construction Automation

A systematic verification framework ensuring quality of construction automation outputs before delivery or deployment.

When to Use

Invoke this skill:

  • After generating cost estimates
  • After creating or updating schedules
  • After processing BIM/CAD data
  • After generating reports (daily, weekly, monthly)
  • After running data pipelines
  • Before submitting documents to clients
  • Before deploying automation workflows

Verification Phases

Phase 1: Data Integrity Check

def verify_data_integrity(output: dict) -> VerificationResult:
    """Check data completeness and consistency"""

    checks = []

    # Completeness check
    required_fields = get_required_fields(output['type'])
    missing = [f for f in required_fields if f not in output]
    checks.append({
        'name': 'Completeness',
        'status': 'PASS' if not missing else 'FAIL',
        'details': f'Missing fields: {missing}' if missing else 'All required fields present'
    })

    # Consistency check
    inconsistencies = find_inconsistencies(output)
    checks.append({
        'name': 'Consistency',
        'status': 'PASS' if not inconsistencies else 'WARN',
        'details': inconsistencies or 'No inconsistencies found'
    })

    # Referential integrity
    broken_refs = check_references(output)
    checks.append({
        'name': 'Referential Integrity',
        'status': 'PASS' if not broken_refs else 'FAIL',
        'details': f'Broken references: {broken_refs}' if broken_refs else 'All references valid'
    })

    return VerificationResult(checks)
Data Integrity Checklist
  • All required fields populated
  • No duplicate records
  • Foreign keys resolve correctly
  • Date formats consistent
  • Currency values formatted correctly
  • Units of measure standardized

Phase 2: Business Logic Verification

def verify_business_logic(output: dict) -> VerificationResult:
    """Verify construction-specific business rules"""

    checks = []

    # Cost estimate checks
    if output['type'] == 'cost_estimate':
        # Verify totals match line items
        calculated_total = sum(item['amount'] for item in output['line_items'])
        declared_total = output['total']
        variance = abs(calculated_total - declared_total)

        checks.append({
            'name': 'Total Accuracy',
            'status': 'PASS' if variance < 0.01 else 'FAIL',
            'details': f'Calculated: {calculated_total}, Declared: {declared_total}'
        })

        # Verify markup applied correctly
        for item in output['line_items']:
            expected_markup = item['base_cost'] * (1 + item['markup_rate'])
            if abs(item['amount'] - expected_markup) > 0.01:
                checks.append({
                    'name': f'Markup Check - {item["id"]}',
                    'status': 'FAIL',
                    'details': f'Expected: {expected_markup}, Got: {item["amount"]}'
                })

    # Schedule checks
    if output['type'] == 'schedule':
        # Verify dependencies
        for task in output['tasks']:
            for pred_id in task.get('predecessors', []):
                pred = find_task(output['tasks'], pred_id)
                if pred and pred['end_date'] > task['start_date']:
                    checks.append({
                        'name': f'Dependency Violation - {task["id"]}',
                        'status': 'FAIL',
                        'details': f'Task starts before predecessor {pred_id} ends'
                    })

        # Verify resource allocation
        resource_conflicts = find_resource_conflicts(output['tasks'])
        checks.append({
            'name': 'Resource Conflicts',
            'status': 'PASS' if not resource_conflicts else 'WARN',
            'details': resource_conflicts or 'No resource conflicts'
        })

    return VerificationResult(checks)

Read the full file on GitHub · 437 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 · 437 lines · 32 tokens per session scan A e1813ea750c4

Subscribe to this mod's changes

verification-loop-construction 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 32 tokens to every session and 3,060 once invoked, about $0.0002 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.