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 quality-control-workflowgit 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/quality-control-workflow)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/quality-control-workflow"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/quality-control-workflow/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/quality-control-workflow"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/quality-control-workflow.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 | $0.00030 | $0.03411 |
| Opus 5 | $0.00015 | $0.01706 |
| Sonnet 5 | $0.00006 | $0.00682 |
| Haiku 4.5 | $0.00003 | $0.00341 |
Grade A, and why
quality-control-workflow 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:
- quality-control-workflow — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Control Workflow
Overview
Automate construction quality control workflows from inspection planning through defect resolution. Track quality metrics, manage non-conformance reports (NCRs), and ensure specification compliance.
"Structured QC workflows reduce rework by 40% and improve first-time quality" — DDC Community
QC Workflow Stages
┌─────────────────────────────────────────────────────────────────┐
│ QC WORKFLOW PIPELINE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Plan → Inspect → Document → Resolve │
│ ──── ─────── ──────── ─────── │
│ 📋 ITP 🔍 Check 📝 NCR 🔧 Fix │
│ 📅 Schedule 📸 Photo 📊 Log ✅ Verify │
│ 👥 Assign 📏 Measure 📧 Notify 📈 Close │
│ │
└─────────────────────────────────────────────────────────────────┘
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Optional
from enum import Enum
from datetime import datetime, timedelta
import json
class QCStatus(Enum):
PENDING = "pending"
IN_PROGRESS = "in_progress"
PASSED = "passed"
FAILED = "failed"
ON_HOLD = "on_hold"
class DefectSeverity(Enum):
CRITICAL = "critical" # Stop work, structural/safety issue
MAJOR = "major" # Must fix before cover-up
MINOR = "minor" # Fix before completion
COSMETIC = "cosmetic" # Punch list item
class NCRStatus(Enum):
DRAFT = "draft"
ISSUED = "issued"
ACCEPTED = "accepted"
DISPUTED = "disputed"
IN_REMEDIATION = "in_remediation"
VERIFIED = "verified"
CLOSED = "closed"
@dataclass
class InspectionPoint:
id: str
name: str
spec_reference: str
acceptance_criteria: str
inspection_method: str
hold_point: bool = False # Requires sign-off before proceeding
witness_point: bool = False # Client/engineer may witness
@dataclass
class QCInspection:
id: str
inspection_point: InspectionPoint
location: str
scheduled_date: datetime
inspector: str
status: QCStatus = QCStatus.PENDING
actual_date: Optional[datetime] = None
result: str = ""
measurements: Dict[str, float] = field(default_factory=dict)
photos: List[str] = field(default_factory=list)
notes: str = ""
sign_off_by: str = ""
@dataclass
class Defect:
id: str
inspection_id: str
description: str
location: str
severity: DefectSeverity
spec_reference: str
photos: List[str] = field(default_factory=list)
assigned_to: str = ""
due_date: Optional[datetime] = None
status: str = "open"
root_cause: str = ""
corrective_action: str = ""
verified_by: str = ""
closed_date: Optional[datetime] = None
@dataclass
class NonConformanceReport:
id: str
defect_ids: List[str]
title: str
description: str
contractor: str
spec_sections: List[str]
status: NCRStatus = NCRStatus.DRAFT
issued_date: Optional[datetime] = None
response_due: Optional[datetime] = None
contractor_response: str = ""
remediation_plan: str = ""
cost_impact: float = 0.0
schedule_impact_days: int = 0
class QualityControlManager:
"""Manage construction quality control workflow."""
def __init__(self, project_id: str, project_name: str):
self.project_id = project_id
self.project_name = project_name
self.inspection_points: Dict[str, InspectionPoint] = {}
self.inspections: Dict[str, QCInspection] = {}
self.defects: Dict[str, Defect] = {}
self.ncrs: Dict[str, NonConformanceReport] = {}
def create_itp(self, work_package: str, inspection_points: List[Dict]) -> List[InspectionPoint]:
"""Create Inspection and Test Plan (ITP)."""
created = []
for idx, point in enumerate(inspection_points):
ip = InspectionPoint(
id=f"{work_package}-{idx+1:03d}",
name=point['name'],
spec_reference=point.get('spec_ref', ''),
acceptance_criteria=point.get('criteria', ''),
inspection_method=point.get('method', 'Visual'),
hold_point=point.get('hold_point', False),
witness_point=point.get('witness_point', False)
)
self.inspection_points[ip.id] = ip
created.append(ip)
return created
def schedule_inspection(self, inspection_point_id: str, location: str,
scheduled_date: datetime, inspector: str) -> QCInspection:
"""Schedule a QC inspection."""
if inspection_point_id not in self.inspection_points:
raise ValueError(f"Inspection point {inspection_point_id} not found")
inspection_id = f"QCI-{datetime.now().strftime('%Y%m%d%H%M%S')}"
inspection = QCInspection(
id=inspection_id,
inspection_point=self.inspection_points[inspection_point_id],
location=location,
scheduled_date=scheduled_date,
inspector=inspector
)
self.inspections[inspection_id] = inspection
return inspection
def conduct_inspection(self, inspection_id: str, result: str,
measurements: Dict[str, float] = None,
photos: List[str] = None,
notes: str = "") -> QCInspection:
"""Record inspection results."""
if inspection_id not in self.inspections:
raise ValueError(f"Inspection {inspection_id} not found")
inspection = self.inspections[inspection_id]
inspection.actual_date = datetime.now()
inspection.result = result
inspection.measurements = measurements or {}
inspection.photos = photos or []
inspection.notes = notes
inspection.status = QCStatus.PASSED if result == "pass" else QCStatus.FAILED
return inspection
def record_defect(self, inspection_id: str, description: str,
location: str, severity: DefectSeverity,
spec_reference: str, photos: List[str] = None,
assigned_to: str = "") -> Defect:
"""Record a defect from inspection."""
defect_id = f"DEF-{datetime.now().strftime('%Y%m%d%H%M%S')}"
# Set due date based on severity
due_days = {
DefectSeverity.CRITICAL: 1,
DefectSeverity.MAJOR: 3,
DefectSeverity.MINOR: 7,
DefectSeverity.COSMETIC: 14
}
defect = Defect(
id=defect_id,
inspection_id=inspection_id,
description=description,
location=location,
severity=severity,
spec_reference=spec_reference,
photos=photos or [],
assigned_to=assigned_to,
due_date=datetime.now() + timedelta(days=due_days[severity])
)
self.defects[defect_id] = defect
return defect
def create_ncr(self, defect_ids: List[str], title: str,
contractor: str, description: str = "") -> NonConformanceReport:
"""Create Non-Conformance Report from defects."""
ncr_id = f"NCR-{datetime.now().strftime('%Y%m%d%H%M%S')}"
# Collect spec references from defects
spec_sections = []
for def_id in defect_ids:
if def_id in self.defects:
spec_sections.append(self.defects[def_id].spec_reference)
ncr = NonConformanceReport(
id=ncr_id,
defect_ids=defect_ids,
title=title,
description=description or self._generate_ncr_description(defect_ids),
contractor=contractor,
spec_sections=list(set(spec_sections)),
response_due=datetime.now() + timedelta(days=5)
)
self.ncrs[ncr_id] = ncr
return ncr
def _generate_ncr_description(self, defect_ids: List[str]) -> str:
"""Generate NCR description from defects."""
lines = ["The following non-conformances have been identified:", ""]
for def_id in defect_ids:
if def_id in self.defects:
d = self.defects[def_id]
lines.append(f"- {d.description} at {d.location}")
lines.append(f" Reference: {d.spec_reference}")
return "\n".join(lines)
def issue_ncr(self, ncr_id: str) -> NonConformanceReport:
"""Issue NCR to contractor."""
if ncr_id not in self.ncrs:
raise ValueError(f"NCR {ncr_id} not found")
ncr = self.ncrs[ncr_id]
ncr.status = NCRStatus.ISSUED
ncr.issued_date = datetime.now()
return ncr
def record_ncr_response(self, ncr_id: str, response: str,
remediation_plan: str, cost_impact: float = 0,
schedule_impact: int = 0) -> NonConformanceReport:
"""Record contractor response to NCR."""
if ncr_id not in self.ncrs:
raise ValueError(f"NCR {ncr_id} not found")
ncr = self.ncrs[ncr_id]
ncr.contractor_response = response
ncr.remediation_plan = remediation_plan
ncr.cost_impact = cost_impact
ncr.schedule_impact_days = schedule_impact
ncr.status = NCRStatus.ACCEPTED
return ncr
def verify_remediation(self, ncr_id: str, verified_by: str) -> NonConformanceReport:
"""Verify NCR remediation is complete."""
if ncr_id not in self.ncrs:
raise ValueError(f"NCR {ncr_id} not found")
ncr = self.ncrs[ncr_id]
ncr.status = NCRStatus.VERIFIED
# Close associated defects
for def_id in ncr.defect_ids:
if def_id in self.defects:
self.defects[def_id].status = "closed"
self.defects[def_id].verified_by = verified_by
self.defects[def_id].closed_date = datetime.now()
return ncr
def get_quality_metrics(self) -> Dict:
"""Calculate quality metrics."""
total_inspections = len(self.inspections)
passed = len([i for i in self.inspections.values() if i.status == QCStatus.PASSED])
failed = len([i for i in self.inspections.values() if i.status == QCStatus.FAILED])
open_defects = len([d for d in self.defects.values() if d.status == "open"])
overdue_defects = len([d for d in self.defects.values()
if d.status == "open" and d.due_date and d.due_date < datetime.now()])
open_ncrs = len([n for n in self.ncrs.values()
if n.status not in [NCRStatus.VERIFIED, NCRStatus.CLOSED]])
return {
"total_inspections": total_inspections,
"passed_inspections": passed,
"failed_inspections": failed,
"first_time_pass_rate": (passed / total_inspections * 100) if total_inspections else 0,
"open_defects": open_defects,
"overdue_defects": overdue_defects,
"open_ncrs": open_ncrs,
"total_cost_impact": sum(n.cost_impact for n in self.ncrs.values()),
"total_schedule_impact": sum(n.schedule_impact_days for n in self.ncrs.values())
}
def generate_qc_report(self) -> str:
"""Generate QC status report."""
metrics = self.get_quality_metrics()
lines = [
f"# Quality Control Report",
f"",
f"**Project:** {self.project_name}",
f"**Date:** {datetime.now().strftime('%Y-%m-%d')}",
f"",
f"## Summary Metrics",
f"",
f"| Metric | Value |",
f"|--------|-------|",
f"| First-Time Pass Rate | {metrics['first_time_pass_rate']:.1f}% |",
f"| Open Defects | {metrics['open_defects']} |",
f"| Overdue Defects | {metrics['overdue_defects']} |",
f"| Open NCRs | {metrics['open_ncrs']} |",
f"| Cost Impact | ${metrics['total_cost_impact']:,.0f} |",
f"| Schedule Impact | {metrics['total_schedule_impact']} days |",
f"",
]
# Critical defects
critical = [d for d in self.defects.values()
if d.severity == DefectSeverity.CRITICAL and d.status == "open"]
if critical:
lines.append("## Critical Open Defects")
for d in critical:
lines.append(f"- **{d.id}**: {d.description}")
lines.append(f" - Location: {d.location}")
lines.append(f" - Due: {d.due_date.strftime('%Y-%m-%d') if d.due_date else 'N/A'}")
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 · 397 lines · 30 tokens per session scan A 7824725b8beb
quality-control-workflow 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 30 tokens to every session and 3,411 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.
Other skills, from other repositories
catchup
Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.
lab:autoresearch
Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.
verify
Verify Elixir/Phoenix changes — compile, format, and test in one loop. Use after implementation, before PRs, or after fixing bugs.
phx-deps-update
Bump outdated Hex deps — inventory, snapshot changelogs, update, fix breaks, split reviewable PRs (patches bundled, majors solo). Use to upgrade/bump Elixir dependencies or when versions fall behind. NOT for deps.get failures (phx-investigate).
mix-compression
Reduce mix output noise (5-15% token savings) by installing rtk filters that compress mix test/credo/dialyzer/compile output before it reaches Claude. Use when long mix output floods context.
testing
Write or repair Elixir tests with ExUnit, sandbox isolation, async reliability, Mox, ExMachina, and LiveViewTest. Use for test files, test setup, or failing/flaky tests. NOT for investigating an application bug outside the test suite.