---

name: docling description: Convert documents (PPTX, PDF, DOCX, XLSX, images) to Markdown/JSON/HTML using IBM Docling. This skill should be used when user asks to convert, parse, or extract content from documents. Triggers on "convert pptx", "parse pdf", "extract from document", "конвертуй презентацію", "витягни з pdf".

Docling - Document Conversion Skill

Convert any document format to structured Markdown, JSON, or HTML using IBM Docling with AI-powered layout analysis.

Quick Start

Batch convert directory to Markdown:

Copyuv run python .claude/skills/docling/scripts/convert_docs.py \
  --input /path/to/documents \
  --output /path/to/output \
  --format md

Single file via CLI:

Copyuv run docling document.pptx --to md --output ./output

Primary Use Case: Batch Conversion

The main script scripts/convert_docs.py handles:

• Recursive directory scanning
• Clean filename generation (slugified, numbered)
• Index file creation with table of contents
• Progress tracking

Example - Convert presentation course:

Copyuv run python .claude/skills/docling/scripts/convert_docs.py \
  --input .artifacts/presentations \
  --output docs/course \
  --format md \
  --prefix "lesson"

Supported Formats

Input Formats

Output Formats

CLI Reference

Basic Usage

Copy# Single file
uv run docling document.pdf --to md

# Multiple files
uv run docling file1.pdf file2.docx --to md

# Directory
uv run docling ./documents/ --to md --output ./output/

# URL
uv run docling https://example.com/doc.pdf --to md

OCR Options

Copy# Enable OCR (default: auto)
uv run docling scanned.pdf --ocr --ocr-engine tesseract

# Force OCR on all content
uv run docling document.pdf --force-ocr

# Specify language
uv run docling document.pdf --ocr-lang ukr,eng

OCR Engines: auto, easyocr, tesseract, tesserocr, rapidocr, ocrmac (macOS)

Table Extraction

Copy# Accurate mode (slower, better quality)
uv run docling document.pdf --table-mode accurate

# Fast mode
uv run docling document.pdf --table-mode fast

# Disable tables
uv run docling document.pdf --no-tables

Image Handling

Copy# Embed images as base64
uv run docling document.pdf --image-export-mode embedded

# Save images as separate files
uv run docling document.pdf --image-export-mode referenced

# Placeholder only (no images)
uv run docling document.pdf --image-export-mode placeholder

AI Enrichment

Copy# Enable code block detection
uv run docling document.pdf --enrich-code

# Enable formula recognition
uv run docling document.pdf --enrich-formula

# Enable picture classification
uv run docling document.pdf --enrich-picture-classification

# Enable picture description (VLM)
uv run docling document.pdf --enrich-picture-description

VLM Pipeline (Vision Language Model)

Copy# Use VLM for complex documents
uv run docling document.pdf --pipeline vlm --vlm-model granite_docling

# Available models: granite_docling, smoldocling, got_ocr_2, granite_vision

ASR Pipeline (Audio Transcription)

Copy# Transcribe audio
uv run docling audio.mp3 --pipeline asr --asr-model whisper_small

# Models: whisper_tiny, whisper_small, whisper_medium, whisper_large, whisper_turbo

Performance Options

Copy# Multi-threading
uv run docling document.pdf --num-threads 8

# GPU acceleration
uv run docling document.pdf --device cuda  # or mps for Apple Silicon

# Timeout per document
uv run docling document.pdf --document-timeout 300

Python API

Basic Conversion

Copyfrom docling.document_converter import DocumentConverter

converter = DocumentConverter()
result = converter.convert("document.pptx")

# Export to Markdown
markdown = result.document.export_to_markdown()

# Export to JSON
json_data = result.document.export_to_dict()

# Save directly
result.document.save_as_markdown("output.md")

Batch Conversion

Copyfrom pathlib import Path
from docling.document_converter import DocumentConverter

converter = DocumentConverter()
sources = list(Path("./documents").glob("*.pdf"))

for result in converter.convert_all(sources):
    if result.status.name == "SUCCESS":
        output = Path("./output") / f"{result.input.file.stem}.md"
        result.document.save_as_markdown(output)

With Pipeline Options

Copyfrom docling.document_converter import DocumentConverter, FormatOption
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions

pdf_options = PdfPipelineOptions(
    do_ocr=True,
    do_table_structure=True,
    table_structure_options={"mode": "accurate"},
)

converter = DocumentConverter(
    format_options={
        InputFormat.PDF: FormatOption(pipeline_options=pdf_options)
    }
)

Chunking for RAG

Copyfrom docling.document_converter import DocumentConverter
from docling.chunking import HybridChunker

converter = DocumentConverter()
result = converter.convert("document.pdf")

chunker = HybridChunker()
chunks = list(chunker.chunk(result.document))

for chunk in chunks:
    print(f"Chunk: {chunk.text[:100]}...")

Troubleshooting

Debug Options

Copyuv run docling document.pdf -v     # info logging
uv run docling document.pdf -vv    # debug logging
uv run docling document.pdf --debug-visualize-layout  # see layout detection

Resources

scripts/

• convert_docs.py - Batch conversion with clean filenames and index generation