# Send habib-pdf-to-json to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "habib-pdf-to-json", "name": "habib-pdf-to-json", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/dbmoradi60/habib-pdf-to-json", "canonicalUrl": "https://clawhub.ai/dbmoradi60/habib-pdf-to-json", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/habib-pdf-to-json", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=habib-pdf-to-json", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "_meta.json" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/habib-pdf-to-json" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/habib-pdf-to-json", "downloadUrl": "https://openagent3.xyz/downloads/habib-pdf-to-json", "agentUrl": "https://openagent3.xyz/skills/habib-pdf-to-json/agent", "manifestUrl": "https://openagent3.xyz/skills/habib-pdf-to-json/agent.json", "briefUrl": "https://openagent3.xyz/skills/habib-pdf-to-json/agent.md" } } ``` ## Documentation ### Overview Based on DDC methodology (Chapter 2.4), this skill transforms unstructured PDF documents into structured formats suitable for analysis and integration. Construction projects generate vast amounts of PDF documentation - specifications, BOMs, schedules, and reports - that need to be extracted and processed. Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form" "Преобразование данных из неструктурированной в структурированную форму — это и искусство, и наука. Этот процесс часто занимает значительную часть работы инженера по обработке данных." — DDC Book, Chapter 2.4 ### ETL Process Overview The conversion follows the ETL pattern: Extract: Load the PDF document Transform: Parse and structure the content Load: Save to CSV, Excel, or JSON ### Quick Start import pdfplumber import pandas as pd # Extract table from PDF with pdfplumber.open("construction_spec.pdf") as pdf: page = pdf.pages[0] table = page.extract_table() df = pd.DataFrame(table[1:], columns=table[0]) df.to_excel("extracted_data.xlsx", index=False) ### Installation # Core libraries pip install pdfplumber pandas openpyxl # For scanned PDFs (OCR) pip install pytesseract pdf2image # Also install Tesseract OCR: https://github.com/tesseract-ocr/tesseract # For advanced PDF operations pip install pypdf ### Extract All Tables from PDF import pdfplumber import pandas as pd def extract_tables_from_pdf(pdf_path): """Extract all tables from a PDF file""" all_tables = [] with pdfplumber.open(pdf_path) as pdf: for page_num, page in enumerate(pdf.pages): tables = page.extract_tables() for table_num, table in enumerate(tables): if table and len(table) > 1: # First row as header df = pd.DataFrame(table[1:], columns=table[0]) df['_page'] = page_num + 1 df['_table'] = table_num + 1 all_tables.append(df) if all_tables: return pd.concat(all_tables, ignore_index=True) return pd.DataFrame() # Usage df = extract_tables_from_pdf("material_specification.pdf") df.to_excel("materials.xlsx", index=False) ### Extract Text with Layout import pdfplumber def extract_text_with_layout(pdf_path): """Extract text preserving layout structure""" full_text = [] with pdfplumber.open(pdf_path) as pdf: for page in pdf.pages: text = page.extract_text() if text: full_text.append(text) return "\\n\\n--- Page Break ---\\n\\n".join(full_text) # Usage text = extract_text_with_layout("project_report.pdf") with open("report_text.txt", "w", encoding="utf-8") as f: f.write(text) ### Extract Specific Table by Position import pdfplumber import pandas as pd def extract_table_from_area(pdf_path, page_num, bbox): """ Extract table from specific area on page Args: pdf_path: Path to PDF file page_num: Page number (0-indexed) bbox: Bounding box (x0, top, x1, bottom) in points """ with pdfplumber.open(pdf_path) as pdf: page = pdf.pages[page_num] cropped = page.within_bbox(bbox) table = cropped.extract_table() if table: return pd.DataFrame(table[1:], columns=table[0]) return pd.DataFrame() # Usage - extract table from specific area # bbox format: (left, top, right, bottom) in points (1 inch = 72 points) df = extract_table_from_area("drawing.pdf", 0, (50, 100, 550, 400)) ### Extract Text from Scanned PDF import pytesseract from pdf2image import convert_from_path import pandas as pd def ocr_scanned_pdf(pdf_path, language='eng'): """ Extract text from scanned PDF using OCR Args: pdf_path: Path to scanned PDF language: Tesseract language code (eng, deu, rus, etc.) """ # Convert PDF pages to images images = convert_from_path(pdf_path, dpi=300) extracted_text = [] for i, image in enumerate(images): text = pytesseract.image_to_string(image, lang=language) extracted_text.append({ 'page': i + 1, 'text': text }) return pd.DataFrame(extracted_text) # Usage df = ocr_scanned_pdf("scanned_specification.pdf", language='eng') df.to_csv("ocr_results.csv", index=False) ### OCR Table Extraction import pytesseract from pdf2image import convert_from_path import pandas as pd import cv2 import numpy as np def ocr_table_from_scanned_pdf(pdf_path, page_num=0): """Extract table from scanned PDF using OCR with table detection""" # Convert specific page to image images = convert_from_path(pdf_path, first_page=page_num+1, last_page=page_num+1, dpi=300) image = np.array(images[0]) # Convert to grayscale gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # Apply thresholding _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV) # Extract text with table structure custom_config = r'--oem 3 --psm 6' text = pytesseract.image_to_string(gray, config=custom_config) # Parse text into table structure lines = text.strip().split('\\n') data = [line.split() for line in lines if line.strip()] if data: # Assume first row is header df = pd.DataFrame(data[1:], columns=data[0] if len(data[0]) > 0 else None) return df return pd.DataFrame() # Usage df = ocr_table_from_scanned_pdf("scanned_bom.pdf") print(df) ### Bill of Materials (BOM) Extraction import pdfplumber import pandas as pd import re def extract_bom_from_pdf(pdf_path): """Extract Bill of Materials from construction PDF""" all_items = [] with pdfplumber.open(pdf_path) as pdf: for page in pdf.pages: tables = page.extract_tables() for table in tables: if not table or len(table) < 2: continue # Find header row (look for common BOM headers) header_keywords = ['item', 'description', 'quantity', 'unit', 'material'] for i, row in enumerate(table): if row and any(keyword in str(row).lower() for keyword in header_keywords): # Found header, process remaining rows headers = [str(h).strip() for h in row] for data_row in table[i+1:]: if data_row and any(cell for cell in data_row if cell): item = dict(zip(headers, data_row)) all_items.append(item) break return pd.DataFrame(all_items) # Usage bom = extract_bom_from_pdf("project_bom.pdf") bom.to_excel("bom_extracted.xlsx", index=False) ### Project Schedule Extraction import pdfplumber import pandas as pd from datetime import datetime def extract_schedule_from_pdf(pdf_path): """Extract project schedule/gantt data from PDF""" with pdfplumber.open(pdf_path) as pdf: all_tasks = [] for page in pdf.pages: tables = page.extract_tables() for table in tables: if not table: continue # Look for schedule-like table headers = table[0] if table else [] # Check if it looks like a schedule schedule_keywords = ['task', 'activity', 'start', 'end', 'duration'] if any(kw in str(headers).lower() for kw in schedule_keywords): for row in table[1:]: if row and any(cell for cell in row if cell): task = dict(zip(headers, row)) all_tasks.append(task) df = pd.DataFrame(all_tasks) # Try to parse dates date_columns = ['Start', 'End', 'Start Date', 'End Date', 'Finish'] for col in date_columns: if col in df.columns: df[col] = pd.to_datetime(df[col], errors='coerce') return df # Usage schedule = extract_schedule_from_pdf("project_schedule.pdf") print(schedule) ### Specification Parsing import pdfplumber import pandas as pd import re def parse_specification_pdf(pdf_path): """Parse construction specification document""" specs = [] with pdfplumber.open(pdf_path) as pdf: full_text = "" for page in pdf.pages: text = page.extract_text() if text: full_text += text + "\\n" # Parse sections (common spec format) section_pattern = r'(\\d+\\.\\d+(?:\\.\\d+)?)\\s+([A-Z][^\\n]+)' sections = re.findall(section_pattern, full_text) for num, title in sections: specs.append({ 'section_number': num, 'title': title.strip(), 'level': len(num.split('.')) }) return pd.DataFrame(specs) # Usage specs = parse_specification_pdf("technical_spec.pdf") print(specs) ### Process Multiple PDFs import pdfplumber import pandas as pd from pathlib import Path def batch_extract_tables(folder_path, output_folder): """Process all PDFs in folder and extract tables""" pdf_files = Path(folder_path).glob("*.pdf") results = [] for pdf_path in pdf_files: print(f"Processing: {pdf_path.name}") try: with pdfplumber.open(pdf_path) as pdf: for page_num, page in enumerate(pdf.pages): tables = page.extract_tables() for table_num, table in enumerate(tables): if table and len(table) > 1: df = pd.DataFrame(table[1:], columns=table[0]) df['_source_file'] = pdf_path.name df['_page'] = page_num + 1 # Save individual table output_name = f"{pdf_path.stem}_p{page_num+1}_t{table_num+1}.xlsx" df.to_excel(Path(output_folder) / output_name, index=False) results.append(df) except Exception as e: print(f"Error processing {pdf_path.name}: {e}") # Combined output if results: combined = pd.concat(results, ignore_index=True) combined.to_excel(Path(output_folder) / "all_tables.xlsx", index=False) return len(results) # Usage count = batch_extract_tables("./pdf_documents/", "./extracted/") print(f"Extracted {count} tables") ### Data Cleaning After Extraction import pandas as pd def clean_extracted_data(df): """Clean common issues in PDF-extracted data""" # Remove completely empty rows df = df.dropna(how='all') # Strip whitespace from string columns for col in df.select_dtypes(include=['object']).columns: df[col] = df[col].str.strip() # Remove rows where all cells are empty strings df = df[df.apply(lambda row: any(cell != '' for cell in row), axis=1)] # Convert numeric columns for col in df.columns: # Try to convert to numeric numeric_series = pd.to_numeric(df[col], errors='coerce') if numeric_series.notna().sum() > len(df) * 0.5: # More than 50% numeric df[col] = numeric_series return df # Usage df = extract_tables_from_pdf("document.pdf") df_clean = clean_extracted_data(df) df_clean.to_excel("clean_data.xlsx", index=False) ### Export Options import pandas as pd import json def export_to_multiple_formats(df, base_name): """Export DataFrame to multiple formats""" # Excel df.to_excel(f"{base_name}.xlsx", index=False) # CSV df.to_csv(f"{base_name}.csv", index=False, encoding='utf-8-sig') # JSON df.to_json(f"{base_name}.json", orient='records', indent=2) # JSON Lines (for large datasets) df.to_json(f"{base_name}.jsonl", orient='records', lines=True) # Usage df = extract_tables_from_pdf("document.pdf") export_to_multiple_formats(df, "extracted_data") ### Quick Reference TaskToolCodeExtract tablepdfplumberpage.extract_table()Extract textpdfplumberpage.extract_text()OCR scannedpytesseractpytesseract.image_to_string(image)Merge PDFspypdfwriter.add_page(page)Convert to imagepdf2imageconvert_from_path(pdf) ### Troubleshooting IssueSolutionTable not detectedTry adjusting table settings: page.extract_table(table_settings={})Wrong column alignmentUse visual debugging: page.to_image().draw_rects()OCR quality poorIncrease DPI, preprocess image, use correct languageMemory issuesProcess pages one at a time, close PDF after processing ### Resources Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.4 Website: https://datadrivenconstruction.io pdfplumber Docs: https://github.com/jsvine/pdfplumber Tesseract OCR: https://github.com/tesseract-ocr/tesseract ### Next Steps See image-to-data for image processing See cad-to-data for CAD/BIM data extraction See etl-pipeline for automated processing workflows See data-quality-check for validating extracted data ## Trust - Source: tencent - Verification: Indexed source record - Publisher: dbmoradi60 - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/habib-pdf-to-json) - [Send to Agent page](https://openagent3.xyz/skills/habib-pdf-to-json/agent) - [JSON manifest](https://openagent3.xyz/skills/habib-pdf-to-json/agent.json) - [Markdown brief](https://openagent3.xyz/skills/habib-pdf-to-json/agent.md) - [Download page](https://openagent3.xyz/downloads/habib-pdf-to-json)