--- name: doc-parser-53ffd8a7ebc2 description: >- Parse complex documents with IBM's open-source Docling library, which turns PDFs and Office files into structured Markdown or JSON. Use when a user asks to parse a document with tables, extract figures from a document, handle multi-column layouts, convert a complex PDF to structured data, extract content from academic papers, or process documents with mixed layouts. Handles tables, figures, headers, footers, and multi-column text. license: Apache-2.0 compatibility: "Requires Python 3.10+ with the docling package (pulls in PyTorch)" metadata: author: terminal-skills version: "1.1.0" category: documents tags: "parsing, docling, tables, figures, layout" repository: https://github.com/docling-project/docling use-cases: "Extract tables and figures from academic papers with complex layouts, Parse financial reports with multi-column formats and embedded charts, Convert complex documents to clean Markdown or JSON preserving structure" agents: "claude-code, openai-codex, gemini-cli, cursor" --- # Document Parser ## Overview Parse complex documents containing tables, figures, multi-column layouts, headers, and mixed content using the Docling library (originally from IBM Research, now an open-source LF AI & Data project). This skill goes beyond simple text extraction by understanding document structure, detecting layout regions, and preserving the logical reading order across complex formatting. ## Instructions When a user asks to parse a complex document or extract structured content from a document with tables, figures, or multi-column layouts, follow these steps: ### Step 1: Install docling ```bash python -m venv .venv && .venv/bin/pip install docling ``` Docling needs Python 3.10+ and depends on PyTorch. On a Linux machine without a GPU, `pip install docling --extra-index-url https://download.pytorch.org/whl/cpu` avoids the large CUDA wheels. The first conversion downloads the layout and table models from Hugging Face, so allow time and network access once. For a quick one-off, the CLI writes Markdown to the output folder (add `--to json` or `--to html` for other formats): ```bash .venv/bin/docling Q3-report.pdf --output converted/ ``` ### Step 2: Load and convert the document Use `DocumentConverter` to parse the document. Reuse one converter for many files; creating it is the expensive part. ```python from docling.document_converter import DocumentConverter converter = DocumentConverter() result = converter.convert("paper.pdf") # a local path or an http(s) URL doc = result.document # a DoclingDocument ``` Supported inputs include PDF, DOCX, XLSX, PPTX, HTML, Markdown, CSV, EPUB, images (PNG, JPEG, TIFF, BMP, WEBP), and more; the current list is in the "Supported formats" page of the docling documentation. Legacy `.doc`/`.xls`/`.ppt` and RTF need LibreOffice installed. To limit work on a long PDF, pass `page_range=(1, 20)` to `convert()`. ### Step 3: Export to the desired format **Markdown** (headings, tables, lists): ```python markdown = doc.export_to_markdown() ``` **Lossless JSON** (the full document tree, reloadable later): ```python import json with open("paper.json", "w") as fp: json.dump(doc.export_to_dict(), fp, indent=2) ``` `doc.save_as_markdown("paper.md")`, `doc.save_as_json("paper.json")` and `doc.save_as_html("paper.html")` write files directly. ### Step 4: Handle specific content types **Tables**: pass the document to `export_to_dataframe` (the argument is required in current versions) and write CSV: ```python from pathlib import Path def save_tables(doc, output_dir="tables"): Path(output_dir).mkdir(exist_ok=True) for i, table in enumerate(doc.tables, start=1): df = table.export_to_dataframe(doc=doc) df.to_csv(f"{output_dir}/table_{i}.csv", index=False) return len(doc.tables) ``` **Figures**: pictures are in `doc.pictures`. To save the image files, ask the PDF pipeline to keep them: ```python from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions from docling.document_converter import DocumentConverter, PdfFormatOption options = PdfPipelineOptions() options.images_scale = 2.0 # about 144 DPI options.generate_picture_images = True converter = DocumentConverter(format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=options)}) doc = converter.convert("paper.pdf").document for n, picture in enumerate(doc.pictures, start=1): page = picture.prov[0].page_no if picture.prov else None print(n, page, picture.caption_text(doc) or "no caption") picture.get_image(doc).save(f"figure_{n}.png") ``` **Reading order and sections**: `for item, level in doc.iterate_items():` walks headings, paragraphs, tables and pictures in reading order. **Scanned pages and images**: OCR runs automatically on bitmap content. Pick the language and engine with `--ocr-lang` / `--ocr-engine` on the CLI, or `PdfPipelineOptions.ocr_options`. Use `--no-ocr` to skip it for born-digital PDFs. ### Step 5: Present the results Format output based on user needs: - **Full document**: Markdown export with all content preserved - **Tables only**: CSV files or formatted markdown tables - **Summary**: Document structure outline with section headings and content types found - **Specific sections**: Extract targeted parts by heading or page range ## Examples ### Example 1: Parse an academic paper **User request:** "Parse this research paper and extract all the tables: paper.pdf" **Actions taken:** 1. Convert paper.pdf using docling 2. Identify 4 tables across 12 pages 3. Export each table as CSV **Output:** ``` Parsed paper.pdf (12 pages) Document structure: - Title: "Machine Learning Approaches for Climate Prediction" - Sections: Abstract, Introduction, Methods, Results, Discussion, References - Tables found: 4 - Figures found: 6 Tables extracted: 1. table_1.csv - "Dataset Characteristics" (5 cols, 8 rows) - Page 4 2. table_2.csv - "Model Hyperparameters" (4 cols, 12 rows) - Page 6 3. table_3.csv - "Performance Comparison" (6 cols, 5 rows) - Page 8 4. table_4.csv - "Ablation Study Results" (5 cols, 7 rows) - Page 9 All tables saved to current directory. ``` ### Example 2: Convert a financial report to Markdown **User request:** "Convert this quarterly report to Markdown so I can edit it: Q3-report.pdf" **Actions taken:** 1. Parse Q3-report.pdf with docling (handles multi-column layout) 2. Export as clean Markdown preserving structure **Output:** ``` Converted Q3-report.pdf to Markdown Document details: - Pages: 24 - Layout: Multi-column (2-column on pages 3-20) - Tables: 8 (all converted to Markdown tables) - Figures: 5 (referenced with captions) - Sections: 12 headings detected Output saved to: Q3-report.md (14,200 words) The multi-column layout has been linearized into single-column reading order. All tables are preserved as Markdown tables. ``` ### Example 3: Pull the line items out of an invoice **User request:** "Parse this invoice and give me the line items as JSON" **Actions taken:** 1. Convert invoice.pdf and take the first table (`doc.tables[0]`), which holds the line items 2. Export it with `export_to_dataframe(doc=doc)` and write `df.to_json(orient="records", indent=2)` 3. Read invoice number, date and totals from the Markdown text, since docling returns layout, not named fields **Output:** ```json [ {"Item": "Office Chairs", "Qty": 10, "Unit Price": "299.99", "Total": "2,999.90"}, {"Item": "Standing Desks", "Qty": 5, "Unit Price": "549.00", "Total": "2,745.00"}, {"Item": "Monitor Arms", "Qty": 15, "Unit Price": "89.50", "Total": "1,342.50"} ] ``` Column names come from the printed header row and cell values are text, so convert amounts to numbers yourself and check the sum against the invoice total. ## Guidelines - Docling handles complex layouts automatically including multi-column text, nested tables, and mixed content. Let it do the heavy lifting. - For very large documents (200+ pages), convert in page ranges (`page_range=(1, 50)`) to manage memory, and use a GPU (`--device cuda`) if one is available. - `--pipeline vlm` runs a vision-language model (GraniteDocling) instead of the default layout and table models; slower, but sometimes better on messy scans. Check results either way. - When tables have merged cells or irregular structures, validate the DataFrame output and flag any parsing anomalies. - Prefer Markdown export for human-readable output and JSON/dict export for programmatic use. - If docling fails to install or parse a specific format, fall back to pdfplumber for PDFs or python-docx for DOCX files. Pass untrusted files only inside a sandbox or container, as with any parser. - Always report the document structure (sections, tables, figures found) before detailed output so the user knows what is available. - For scanned documents without a text layer, docling may use its built-in OCR. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/doc-parser/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Marketing, Product Management, Content & Media Roles: Data Analyst, Researcher, Content Marketer, Product Manager, Technical Writer Specialties: API, Backend Tools: Docling Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 2f0a0ac2bb4f7fb85128d2b3cfb22fa40a15c3108b29c8c8c272e8afe200315b