DDC Skills Collection for AI Coding Assistants

AI Tools for Construction Company Automation

Works with any AI coding assistant:

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What is this?

A collection of 238 skills for automating construction company processes with AI coding assistants.

What is a "Skill"?

A skill is a SKILL.md file — structured instructions that an AI coding assistant can read and execute. Each skill describes a specific task: what problem it solves, what code to generate, and what tools are needed. You open a skill folder in your AI assistant, and it helps you implement the described functionality.

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Collection Structure

Skills are organized by source and complexity level:

Category	What's inside	Skills	Start here if...
1_DDC_Toolkit	Production-ready tools: CWICR database, CAD converters, analytics	85	You need a working tool now
2_DDC_Book	Skills mapped to book chapters: data evolution, types, estimation, ML	67	You want to follow a structured learning path
3_DDC_Insights	Practical workflows: n8n automation, AI agents, field tools	20	You need workflow automation
4_DDC_Curated	Document generation (PDF, Excel, DOCX, PPTX), quality checks	20	You need document or report templates
5_DDC_Innovative	Advanced: computer vision, IoT, digital twins, risk assessment, AI agents, ESG, EU AI Act, circular construction, generative design	34	You're ready for AI/ML experimentation
6_OpenConstructionERP	Work with the OpenConstructionERP platform: load cost bases, BOQ & estimating, BIM takeoff, 4D/5D, cost browser, MCP, field ops, tendering, validation, geo/coordination, property development	12	You build on the open-source construction ERP

mindmap
  root((DDC Skills<br/>238 skills))
    1_DDC_Toolkit
      CWICR Database
        8 national bases
        30 markets · 26 languages
      CAD Converters
        RVT → Excel
        IFC → Excel
        DWG → Excel
      Analytics
        KPI Dashboard
        Cost Analysis
    2_DDC_Book
      Part I: Data Evolution
      Part II: Data Types
      Part III: Estimation
      Part IV: Analytics & ML
      Part V: Threats & Strategy
    3_DDC_Insights
      n8n Workflows
        Daily Reports
        Photo Reports
      AI Agents 2026
      Field Automation
    4_DDC_Curated
      Document Generation
        PDF, Excel
        DOCX, PPTX
      Quality Assurance
    5_DDC_Innovative
      AI/ML Skills
        Defect Detection
        Risk Assessment
      IoT & Sensors
      Advanced BIM
        Digital Twin
      AI Agent Orchestration
      Embodied Carbon & ESG
      EU AI Act
      Material Passports
      Generative Design
    6_OpenConstructionERP
      Load Cost Bases
        TR · ZH · BR · ES · IT
        VN · ID · GR · 30 markets
      BOQ & Estimating
      BIM & CAD Takeoff
      4D/5D Scheduling
      Cost Browser
      MCP Integration
      Field Ops · Tendering
      Validation · Geo Hub
      Property Development

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Where to Start

If you manage or lead a construction company:

Step 1. Read GETTING_STARTED.md — a non-technical overview of what can be automated and how to prioritize.

Step 2. Download the Data-Driven Construction book (free, 31 languages) — it explains the methodology behind these skills: how to assess your company's data maturity, identify bottlenecks, and plan digital transformation.

Step 3. Identify your biggest pain point in the table below and start with the corresponding skill.

If you are a developer or IT lead:

Step 1. Install prerequisites (see Prerequisites).

Step 2. Pick a skill from the table below, open its folder in your AI assistant, and follow the SKILL.md.

Step 3. Adapt the generated code to your data and deploy.

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What Can You Automate?

Examples of common problems and which skills address them:

Your problem	What the skill does	Skill to run	Folder
Searching for work item rates takes too long	Semantic search across 78,228 national + 55,719 global items in 26 languages	semantic-search-cwicr	1_DDC_Toolkit/
Estimators spend days building estimates manually	Generates estimates from historical data and templates	estimate-builder	1_DDC_Toolkit/
BIM models contain data but it's locked in RVT/IFC files	Extracts quantities and properties to Excel	ifc-to-excel, rvt-to-excel	1_DDC_Toolkit/
Daily/weekly reports take hours to compile	Automated data collection and report generation	n8n-daily-report	3_DDC_Insights/
Site photos pile up with no organization	AI classifies and tags site photos automatically	n8n-photo-report	3_DDC_Insights/
Data is scattered across Excel files, emails, PDFs	Finds all data sources and maps dependencies	data-silo-detection	2_DDC_Book/
No visibility into project KPIs	Dashboard with real-time metrics from your data	kpi-dashboard	1_DDC_Toolkit/
Budget overruns discovered too late	Scheduled budget vs. actual comparison	budget-tracker	1_DDC_Toolkit/
PDF specifications need to be searchable	Extracts text and tables from PDFs into structured data	specification-extractor	2_DDC_Book/
Schedule delays are hard to predict	Statistical analysis of schedule variance patterns	schedule-delay-analyzer	3_DDC_Insights/

This is a selection of examples. The full collection contains 221 skills covering estimation, reporting, BIM, document processing, analytics, and more.

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How to Use a Skill

1. Clone this repository
   git clone https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git

2. Open a skill folder in your AI assistant
   cd DDC_Skills_for_AI_Agents_in_Construction/1_DDC_Toolkit/CWICR-Database/semantic-search-cwicr/

3. The assistant reads SKILL.md and generates the code for you

4. Review, adapt to your data, and run

Example: ETL Pipeline

# Automatic processing of all Excel files from a folder

import pandas as pd
from pathlib import Path

# Extract
all_data = [pd.read_excel(f) for f in Path("./estimates/").glob("*.xlsx")]
df = pd.concat(all_data)

# Transform
df['Total'] = df['Quantity'] * df['Unit_Price']
summary = df.groupby('Category')['Total'].sum()

# Load
summary.to_excel("summary_report.xlsx")

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Implementation Path

A typical automation project follows these stages. You don't need to do all of them — start with what solves your most pressing problem.

Stage	What you do	Which skills help
1. Audit	List all data sources in your company (Excel files, databases, BIM models, PDFs, emails)	data-silo-detection, data-source-audit
2. Classify	Understand what data you have: structured, semi-structured, or unstructured	data-type-classifier, data-profiler
3. Connect	Build ETL pipelines to extract and normalize data	etl-pipeline, ifc-to-excel, specification-extractor
4. Automate	Set up recurring reports, alerts, and dashboards	n8n-daily-report, kpi-dashboard, budget-tracker
5. Analyze	Apply analytics and ML to find patterns and predict outcomes	cost-prediction, schedule-forecaster, risk-assessment

Each stage builds on the previous one, but you can enter at any point depending on your current state.

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How Does It Work?

Each step in the implementation path corresponds to specific skills and tools:

flowchart LR
    subgraph S1["STEP 1"]
        A[Audit<br/>Find Data Silos]
    end

    subgraph S2["STEP 2"]
        B[Classify<br/>Data Types]
    end

    subgraph S3["STEP 3"]
        C[Connect<br/>ETL Pipelines]
    end

    subgraph S4["STEP 4"]
        D[Automate<br/>Reports & Dashboards]
    end

    subgraph S5["STEP 5"]
        E[Analyze<br/>ML & Predictions]
    end

    A --> B --> C --> D --> E

    A1[data-silo-detection<br/>data-source-audit] -.-> A
    B1[data-type-classifier<br/>data-profiler] -.-> B
    C1[etl-pipeline<br/>ifc-to-excel] -.-> C
    D1[n8n-daily-report<br/>kpi-dashboard] -.-> D
    E1[cost-prediction<br/>schedule-forecaster] -.-> E

    style S1 fill:#ffebee
    style S2 fill:#fff3e0
    style S3 fill:#e8f5e9
    style S4 fill:#e3f2fd
    style S5 fill:#f3e5f5

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Data Types in Construction

Construction data comes in three forms. Each requires a different processing approach, and each has corresponding skills:

flowchart TB
    subgraph STRUCTURED["STRUCTURED"]
        S1[Excel]
        S2[SQL Database]
        S3[CSV]
    end

    subgraph SEMI["SEMI-STRUCTURED"]
        M1[IFC/BIM]
        M2[JSON]
        M3[XML]
    end

    subgraph UNSTRUCTURED["UNSTRUCTURED"]
        U1[PDF]
        U2[Photos]
        U3[Scans]
    end

    STRUCTURED -->|SQL queries, pandas| DB[(Central<br/>Database)]
    SEMI -->|ifcopenshell, parsers| DB
    UNSTRUCTURED -->|AI/OCR, pdfplumber| DB

    DB --> AUTO[Automation & Analytics]

    style STRUCTURED fill:#c8e6c9
    style SEMI fill:#fff9c4
    style UNSTRUCTURED fill:#ffcdd2
    style DB fill:#e1f5fe
    style AUTO fill:#f3e5f5

Data type	Examples	Skills that process it
Structured	Excel, CSV, SQL databases	etl-pipeline, estimate-builder, budget-tracker
Semi-structured	IFC/BIM models, JSON, XML	ifc-to-excel, rvt-to-excel, dwg-to-excel
Unstructured	PDF documents, photos, scans	specification-extractor, n8n-photo-report, document-ocr

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Prerequisites

Requirement	Details
Python 3.9+	Most skills use Python scripts
AI Coding Assistant	Claude Code, Cursor, Copilot, or similar
Basic Python knowledge	Ability to run scripts and install packages
Your data	Excel files, PDFs, or BIM models to process

Optional for advanced skills:

• Docker (for n8n workflows)
• PostgreSQL or SQLite (for database skills)
• OpenAI API key (for LLM-based skills)

Installation

pip install pandas openpyxl ifcopenshell pdfplumber

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Data Flow in Construction

Skills cover the typical data pipeline: from raw input files to processed, actionable outputs.

flowchart LR
    subgraph INPUT["📥 YOUR DATA"]
        A1[Excel Estimates]
        A2[Revit/IFC Models]
        A3[Site Photos]
        A4[PDF Documents]
    end

    subgraph DDC["⚙️ DDC SKILLS"]
        B1[ETL Pipeline]
        B2[IFC Parser]
        B3[AI Analysis]
        B4[Document OCR]
    end

    subgraph OUTPUT["📤 RESULTS"]
        C1[Auto Reports]
        C2[Auto Estimates]
        C3[Progress Tracking]
        C4[Searchable Data]
    end

    A1 --> B1 --> C1
    A2 --> B2 --> C2
    A3 --> B3 --> C3
    A4 --> B4 --> C4

    style INPUT fill:#e1f5fe
    style DDC fill:#fff3e0
    style OUTPUT fill:#e8f5e9

Construction data comes in three forms. Each requires a different processing approach:

Data type	Examples	How skills process it
Structured	Excel, CSV, SQL databases	Direct queries and transformations
Semi-structured	IFC/BIM models, JSON, XML	Parsing with specialized libraries
Unstructured	PDF documents, photos, scans	AI/OCR extraction to structured format

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Folder Structure

DDC_Skills/
│
├── 1_DDC_Toolkit/              ← Production tools (85 skills)
│   ├── CWICR-Database/         ← 8 national bases + 30 markets database
│   ├── CAD-Converters/         ← Revit/IFC/DWG → Excel
│   └── ...
│
├── 2_DDC_Book/                 ← Skills from the book (67 skills)
│   ├── 1.1-Data-Evolution/     ← Digital maturity assessment
│   ├── 1.2-Data-Silos-Integration/  ← Find & connect data sources
│   ├── 3.1-Cost-Estimation/    ← Build estimates from data
│   ├── 4.2-ETL-Automation/     ← Automate data pipelines
│   └── ...
│
├── 3_DDC_Insights/             ← Practical workflows (20 skills)
│   ├── Automation-Workflows/   ← n8n automation
│   ├── AI-Agents/              ← Multi-agent systems (2026)
│   ├── Field-Automation/       ← Telegram bot, voice reports
│   └── Open-Data-Transparency/ ← Uberization readiness
│
├── 4_DDC_Curated/              ← External skills (20 skills)
│   ├── Document-Generation/    ← PDF/Excel/DOCX/PPTX generation
│   └── Quality-Assurance/      ← Quality checks
│
├── 5_DDC_Innovative/           ← Advanced AI/ML skills (29 skills)
│   ├── defect-detection-ai/    ← Computer vision for defects
│   ├── digital-twin-sync/      ← Real-time BIM sync
│   └── ...
│
├── Books/                      ← Free book downloads (31 languages)
├── GETTING_STARTED.md          ← START HERE
└── README.md                   ← You are here

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Documentation

Document	Description	Audience
GETTING_STARTED.md	Step-by-step automation guide	Executives, beginners
OPTIMIZER_GUIDE.md	How to work effectively with AI assistants	Developers
IMPROVEMENT_ROADMAP.md	Collection development plan	Contributors

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Potential Time Savings

Automation results vary depending on company size, data quality, and implementation effort:

Process	Manual approach	With automation
Find work item rate	Search through price books	Database query with filters
Daily report compilation	Collect data from multiple sources	Pre-configured data aggregation
IFC quantity extraction	Open model, measure manually	Script-based extraction to Excel
Budget variance tracking	Weekly spreadsheet updates	Scheduled comparison reports
Document organization	Manual folder sorting	Metadata-based classification

Results depend on data preparation and workflow complexity. See GETTING_STARTED.md for implementation guidance.

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About the Book

The majority of skills are based on the book "Data-Driven Construction" — a methodology for digital transformation in construction. The book explains the reasoning behind each skill category: why data silos matter, how to classify your data, and what to automate first.

Book structure maps to skills: Part I — Data landscape assessment → 2_DDC_Book/1.1-*, 1.2-* Part II — Data types and classification → 2_DDC_Book/2.1-* through 2.6-* Part III — Estimation and scheduling → 2_DDC_Book/3.1-* through 3.3-* Part IV — Analytics and ML → 2_DDC_Book/4.1-* through 4.5-* Part V — Threats and long-term strategy → 2_DDC_Book/5.* Download Free in 31 Languages →

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Resources

Resource	Link
Book (All Languages)	https://datadrivenconstruction.io/books/
Website	https://datadrivenconstruction.io
CWICR Demo	https://openconstructionestimate.com
GitHub	https://github.com/datadrivenconstruction
CWICR Database	https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR
CAD2Data Pipeline	https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN-pipeline-with-conversion-validation-qto

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Support the Project

If you find these tools useful, please give the repository a star — it helps others discover these resources.

Related repositories:

Repository	Description
OpenConstructionEstimate-DDC-CWICR	8 national bases + 30 markets in 26 languages
cad2data Pipeline	Revit/IFC/DWG/DGN to Excel converter

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Contributing

Contributions are welcome:

• Report issues — bugs, unclear documentation, broken links
• Suggest skills — describe the automation you need
• Submit PRs — new skills, improvements, translations

Each skill should include a SKILL.md with clear instructions and working code examples.

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License

• CWICR Database: CC BY-NC 4.0 (non-commercial; separate DDC commercial license)
• DDC Tools: MIT License
• Skills: MIT License

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Trademarks

All product names, logos, and brands mentioned in this repository are property of their respective owners. All company, product and service names used are for identification purposes only. Use of these names, logos, and brands does not imply endorsement.

• IFC is an open standard by buildingSMART International
• File format references (RVT, DWG, DGN, NWD, etc.) are used for technical interoperability purposes only

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Start here → GETTING_STARTED.md