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drawing-analyzer绘图分析仪

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

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1,747

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:drawing-analyzer(绘图分析仪)
来源仓库:https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
仓库路径:skills/drawing-analyzer
安装命令:
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill drawing-analyzer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill drawing-analyzer

简介

自动解析建筑图纸(PDF/DWG)以提取尺寸与注释信息。

  • 支持标题块数据抓取与符号标准化识别。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 加速工程量计算与设计规范符合性检查。
  • 需上传图纸文件并指定分析目标区域或构件类型。
  • drawing-analyzer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Drawing Analyzer for Construction

Overview

Analyze construction drawings (PDF, DWG) to extract dimensions, annotations, symbols, title block data, and support automated quantity takeoff and design review.

Business Case

Drawing analysis automation enables:

  • Faster Takeoffs: Extract quantities from drawings
  • Quality Control: Verify drawing completeness
  • Data Extraction: Pull metadata for project systems
  • Design Review: Automated checking against standards

Technical Implementation

from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import re
import pdfplumber
from pathlib import Path

@dataclass
class TitleBlockData:
    project_name: str
    project_number: str
    sheet_number: str
    sheet_title: str
    discipline: str
    scale: str
    date: str
    revision: str
    drawn_by: str
    checked_by: str
    approved_by: str

@dataclass
class Dimension:
    value: float
    unit: str
    dimension_type: str  # linear, angular, radial
    location: Tuple[float, float]
    associated_text: str

@dataclass
class Annotation:
    text: str
    annotation_type: str  # note, callout, tag, keynote
    location: Tuple[float, float]
    references: List[str]

@dataclass
class Symbol:
    symbol_type: str  # door, window, equipment, etc.
    tag: str
    location: Tuple[float, float]
    properties: Dict[str, Any]

@dataclass
class DrawingAnalysisResult:
    file_name: str
    title_block: Optional[TitleBlockData]
    dimensions: List[Dimension]
    annotations: List[Annotation]
    symbols: List[Symbol]
    scale_factor: float
    drawing_area: Tuple[float, float]
    quality_issues: List[str]

class DrawingAnalyzer:
    """Analyze construction drawings for data extraction."""

    # Common dimension patterns
    DIMENSION_PATTERNS = [
        r"(\d+'-\s*\d+(?:\s*\d+/\d+)?\"?)",  # Feet-inches: 10'-6", 10' - 6 1/2"
        r"(\d+(?:\.\d+)?)\s*(?:mm|cm|m|ft|in)",  # Metric/imperial with unit
        r"(\d+'-\d+\")",  # Compact feet-inches
        r"(\d+)\s*(?:SF|LF|CY|EA)",  # Quantity dimensions
    ]

    # Common annotation patterns
    ANNOTATION_PATTERNS = {
        'keynote': r'^\d{1,2}[A-Z]?$',  # 1A, 12, 5B
        'room_tag': r'^(?:RM|ROOM)\s*\d+',
        'door_tag': r'^[A-Z]?\d{2,3}[A-Z]?$',
        'grid_line': r'^[A-Z]$|^\d+$',
        'elevation': r'^(?:EL|ELEV)\.?\s*\d+',
        'detail_ref': r'^\d+/[A-Z]\d+',
    }

    # Scale patterns
    SCALE_PATTERNS = [
        r"SCALE:\s*(\d+(?:/\d+)?)\s*[\"']\s*=\s*(\d+)\s*['\-]",  # 1/4" = 1'-0"
        r"(\d+):(\d+)",  # 1:100
        r"NTS|NOT TO SCALE",
    ]

    def __init__(self):
        self.results: Dict[str, DrawingAnalysisResult] = {}

    def analyze_pdf_drawing(self, pdf_path: str) -> DrawingAnalysisResult:
        """Analyze a PDF drawing."""
        path = Path(pdf_path)

        all_text = ""
        dimensions = []
        annotations = []
        symbols = []
        quality_issues = []

        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                # Extract text
                text = page.extract_text() or ""
                all_text += text + "\n"

                # Extract dimensions
                page_dims = self._extract_dimensions(text)
                dimensions.extend(page_dims)

                # Extract annotations
                page_annots = self._extract_annotations(text)
                annotations.extend(page_annots)

                # Extract from tables (often contain schedules)
                tables = page.extract_tables()
                for table in tables:
                    symbols.extend(self._parse_schedule_table(table))

        # Parse title block
        title_block = self._extract_title_block(all_text)

        # Determine scale
        scale_factor = self._determine_scale(all_text)

        # Quality checks
        quality_issues = self._check_drawing_quality(
            title_block, dimensions, annotations
        )

        result = DrawingAnalysisResult(
            file_name=path.name,
            title_block=title_block,
            dimensions=dimensions,
            annotations=annotations,
            symbols=symbols,
            scale_factor=scale_factor,
            drawing_area=(0, 0),  # Would need image analysis
            quality_issues=quality_issues
        )

        self.results[path.name] = result
        return result

    def _extract_dimensions(self, text: str) -> List[Dimension]:
        """Extract dimensions from text."""
        dimensions = []

        for pattern in self.DIMENSION_PATTERNS:
            matches = re.findall(pattern, text)
            for match in matches:
                value, unit = self._parse_dimension_value(match)
                if value > 0:
                    dimensions.append(Dimension(
                        value=value,
                        unit=unit,
                        dimension_type='linear',
                        location=(0, 0),
                        associated_text=match
                    ))

        return dimensions

    def _parse_dimension_value(self, dim_text: str) -> Tuple[float, str]:
        """Parse dimension text to value and unit."""
        dim_text = dim_text.strip()

        # Feet and inches: 10'-6"
        ft_in_match = re.match(r"(\d+)'[-\s]*(\d+)?(?:\s*(\d+)/(\d+))?\"?", dim_text)
        if ft_in_match:
            feet = int(ft_in_match.group(1))
            inches = int(ft_in_match.group(2) or 0)
            if ft_in_match.group(3) and ft_in_match.group(4):
                inches += int(ft_in_match.group(3)) / int(ft_in_match.group(4))
            return feet * 12 + inches, 'in'

        # Metric with unit
        metric_match = re.match(r"(\d+(?:\.\d+)?)\s*(mm|cm|m)", dim_text)
        if metric_match:
            return float(metric_match.group(1)), metric_match.group(2)

        # Just a number
        num_match = re.match(r"(\d+(?:\.\d+)?)", dim_text)
        if num_match:
            return float(num_match.group(1)), ''

        return 0, ''

    def _extract_annotations(self, text: str) -> List[Annotation]:
        """Extract annotations from text."""
        annotations = []
        lines = text.split('\n')

        for line in lines:
            line = line.strip()
            if not line:
                continue

            for annot_type, pattern in self.ANNOTATION_PATTERNS.items():
                if re.match(pattern, line, re.IGNORECASE):
                    annotations.append(Annotation(
                        text=line,
                        annotation_type=annot_type,
                        location=(0, 0),
                        references=[]
                    ))
                    break

            # General notes
            if line.startswith(('NOTE:', 'SEE ', 'REFER TO', 'TYP', 'U.N.O.')):
                annotations.append(Annotation(
                    text=line,
                    annotation_type='note',
                    location=(0, 0),
                    references=[]
                ))

        return annotations

    def _extract_title_block(self, text: str) -> Optional[TitleBlockData]:
        """Extract title block information."""
        # Common title block patterns
        patterns = {
            'project_name': r'PROJECT(?:\s*NAME)?:\s*(.+?)(?:\n|$)',
            'project_number': r'(?:PROJECT\s*)?(?:NO|NUMBER|#)\.?:\s*(\S+)',
            'sheet_number': r'SHEET(?:\s*NO)?\.?:\s*([A-Z]?\d+(?:\.\d+)?)',
            'sheet_title': r'SHEET\s*TITLE:\s*(.+?)(?:\n|$)',
            'scale': r'SCALE:\s*(.+?)(?:\n|$)',
            'date': r'DATE:\s*(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})',
            'revision': r'REV(?:ISION)?\.?:\s*(\S+)',
            'drawn_by': r'(?:DRAWN|DRN)\s*(?:BY)?:\s*(\S+)',
            'checked_by': r'(?:CHECKED|CHK)\s*(?:BY)?:\s*(\S+)',
        }

        extracted = {}
        for field, pattern in patterns.items():
            match = re.search(pattern, text, re.IGNORECASE)
            extracted[field] = match.group(1).strip() if match else ''

        # Determine discipline from sheet number
        sheet_num = extracted.get('sheet_number', '')
        discipline = ''
        if sheet_num:
            prefix = sheet_num[0].upper() if sheet_num[0].isalpha() else ''
            discipline_map = {
                'A': 'Architectural', 'S': 'Structural', 'M': 'Mechanical',
                'E': 'Electrical', 'P': 'Plumbing', 'C': 'Civil',
                'L': 'Landscape', 'I': 'Interior', 'F': 'Fire Protection'
            }
            discipline = discipline_map.get(prefix, '')

        return TitleBlockData(
            project_name=extracted.get('project_name', ''),
            project_number=extracted.get('project_number', ''),
            sheet_number=sheet_num,
            sheet_title=extracted.get('sheet_title', ''),
            discipline=discipline,
            scale=extracted.get('scale', ''),
            date=extracted.get('date', ''),
            revision=extracted.get('revision', ''),
            drawn_by=extracted.get('drawn_by', ''),
            checked_by=extracted.get('checked_by', ''),
            approved_by=''
        )

    def _parse_schedule_table(self, table: List[List]) -> List[Symbol]:
        """Parse schedule table to extract symbols/elements."""
        symbols = []

        if not table or len(table) < 2:
            return symbols

        # First row is usually headers
        headers = [str(cell).lower() if cell else '' for cell in table[0]]

        # Find key columns
        tag_col = next((i for i, h in enumerate(headers) if 'tag' in h or 'mark' in h or 'no' in h), 0)
        type_col = next((i for i, h in enumerate(headers) if 'type' in h or 'size' in h), -1)

        for row in table[1:]:
            if len(row) > tag_col and row[tag_col]:
                tag = str(row[tag_col]).strip()
                symbol_type = str(row[type_col]).strip() if type_col >= 0 and len(row) > type_col else ''

                if tag:
                    props = {}
                    for i, header in enumerate(headers):
                        if i < len(row) and row[i]:
                            props[header] = str(row[i])

                    symbols.append(Symbol(
                        symbol_type=symbol_type or 'unknown',
                        tag=tag,
                        location=(0, 0),
                        properties=props
                    ))

        return symbols

    def _determine_scale(self, text: str) -> float:
        """Determine drawing scale factor."""
        for pattern in self.SCALE_PATTERNS:
            match = re.search(pattern, text, re.IGNORECASE)
            if match:
                if 'NTS' in match.group(0).upper():
                    return 0  # Not to scale

                if '=' in match.group(0):
                    # Imperial: 1/4" = 1'-0"
                    return self._parse_imperial_scale(match.group(0))
                else:
                    # Metric: 1:100
                    return 1 / float(match.group(2))

        return 1.0  # Default

    def _parse_imperial_scale(self, scale_text: str) -> float:
        """Parse imperial scale to factor."""
        match = re.search(r'(\d+)(?:/(\d+))?\s*["\']?\s*=\s*(\d+)', scale_text)
        if match:
            numerator = float(match.group(1))
            denominator = float(match.group(2)) if match.group(2) else 1
            feet = float(match.group(3))
            inches_per_foot = (numerator / denominator)
            return inches_per_foot / (feet * 12)
        return 1.0

    def _check_drawing_quality(self, title_block: TitleBlockData,
                                dimensions: List, annotations: List) -> List[str]:
        """Check drawing for quality issues."""
        issues = []

        if title_block:
            if not title_block.project_number:
                issues.append("Missing project number in title block")
            if not title_block.sheet_number:
                issues.append("Missing sheet number")
            if not title_block.scale:
                issues.append("Missing scale indication")
            if not title_block.date:
                issues.append("Missing date")

        if len(dimensions) == 0:
            issues.append("No dimensions found - verify drawing content")

        # Check for typical construction notes
        note_types = [a.annotation_type for a in annotations]
        if 'note' not in note_types:
            issues.append("No general notes found")

        return issues

    def generate_drawing_index(self, results: List[DrawingAnalysisResult]) -> str:
        """Generate drawing index from multiple analyzed drawings."""
        lines = ["# Drawing Index", ""]
        lines.append("| Sheet | Title | Discipline | Scale | Rev |")
        lines.append("|-------|-------|------------|-------|-----|")

        for result in sorted(results, key=lambda r: r.title_block.sheet_number if r.title_block else ''):
            if result.title_block:
                tb = result.title_block
                lines.append(f"| {tb.sheet_number} | {tb.sheet_title} | {tb.discipline} | {tb.scale} | {tb.revision} |")

        return "\n".join(lines)

    def generate_report(self, result: DrawingAnalysisResult) -> str:
        """Generate analysis report for a drawing."""
        lines = ["# Drawing Analysis Report", ""]
        lines.append(f"**File:** {result.file_name}")

        if result.title_block:
            tb = result.title_block
            lines.append("")
            lines.append("## Title Block")
            lines.append(f"- **Project:** {tb.project_name}")
            lines.append(f"- **Project No:** {tb.project_number}")
            lines.append(f"- **Sheet:** {tb.sheet_number}")
            lines.append(f"- **Title:** {tb.sheet_title}")
            lines.append(f"- **Discipline:** {tb.discipline}")
            lines.append(f"- **Scale:** {tb.scale}")
            lines.append(f"- **Date:** {tb.date}")
            lines.append(f"- **Revision:** {tb.revision}")

        lines.append("")
        lines.append("## Content Summary")
        lines.append(f"- **Dimensions Found:** {len(result.dimensions)}")
        lines.append(f"- **Annotations Found:** {len(result.annotations)}")
        lines.append(f"- **Symbols/Elements:** {len(result.symbols)}")

        if result.quality_issues:
            lines.append("")
            lines.append("## Quality Issues")
            for issue in result.quality_issues:
                lines.append(f"- ⚠️ {issue}")

        if result.symbols:
            lines.append("")
            lines.append("## Elements Found")
            for symbol in result.symbols[:20]:
                lines.append(f"- {symbol.tag}: {symbol.symbol_type}")

        return "\n".join(lines)

Quick Start

# Initialize analyzer
analyzer = DrawingAnalyzer()

# Analyze a drawing
result = analyzer.analyze_pdf_drawing("A101_Floor_Plan.pdf")

# Check title block
if result.title_block:
    print(f"Sheet: {result.title_block.sheet_number}")
    print(f"Title: {result.title_block.sheet_title}")
    print(f"Scale: {result.title_block.scale}")

# Review extracted data
print(f"Dimensions: {len(result.dimensions)}")
print(f"Annotations: {len(result.annotations)}")
print(f"Symbols: {len(result.symbols)}")

# Check quality
for issue in result.quality_issues:
    print(f"Issue: {issue}")

# Generate report
report = analyzer.generate_report(result)
print(report)

Dependencies

pip install pdfplumber

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.75%
按下载量换算208

Claude

27.88%
按下载量换算158

Cursor

18.08%
按下载量换算102

Gemini CLI

9.56%
按下载量换算54

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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