docling

name	docling
description	Convert PDFs, Office files, HTML, Markdown, images, and other documents into structured outputs with Docling; includes install, conversion, batch, chunking, advanced PDF guidance, and DOCX/OOXML interoperability fallbacks.
version	1.1.1
author	Imported local docling pack, adapted by Hermes Agent
license	Unknown
metadata	{"hermes":{"tags":["docling","documents","pdf","conversion","ocr","chunking"],"category":"user-imported","related_skills":["obsidian","clip"]}}

Hermes compatibility note

• This local pack was installed into Hermes as docling.
• Task documents were mirrored into references/ so they are directly accessible via skill_view linked files.
• Reusable helpers remain under scripts/.
• The original imported folder layout (tasks/, examples/, scripts/) was preserved.

Docling Skill (Install • Convert • Export • Chunk • Batch • Advanced PDF)

Use this skill when you need to convert PDFs, images, Office files, HTML, Markdown, CSV, or XML into structured outputs with Docling.

This pack is based on the official Docling documentation, especially the installation, usage, CLI reference, and example pages.

Core operating rules

1. Prefer DocumentConverter first. The official docs recommend the high-level DocumentConverter API for most work. Drop to specific backends only when you have a clear reason.
2. Single-document workflow: convert one source -> export the formats you need -> inspect the output -> adjust pipeline options only if quality requires it.
3. Batch workflow: use convert_all(..., raises_on_error=False) so you can inspect partial failures instead of stopping on the first bad file.
4. Chunking workflow: convert to a Docling document first, then run HybridChunker; for embeddings, prefer the text returned by chunker.contextualize(...).
5. Remote services are opt-in. If a pipeline talks to an external service, you must explicitly enable it.
6. Offline use is first-class. You can pre-download models and point Docling to a local artifacts directory.

Quick start

# Install the base package
pip install docling

# Optional: for hybrid chunking
pip install transformers

# Convert one file with the packaged helper
python scripts/docling_convert.py input.pdf --output-dir out --to md json text

# DOC/DOCX fallback helpers (LibreOffice / pandoc / OOXML ZIP workflows)
python scripts/docling_docx_tools.py convert old.doc docx --output-dir converted
python scripts/docling_docx_tools.py extract revised.docx revised.md --track-changes all
python scripts/docling_docx_tools.py unpack report.docx unpacked_report

# Batch convert a folder
python scripts/docling_batch.py docs/*.pdf --output-dir out_batch --to md json

# Chunk a document into JSONL
python scripts/docling_chunk.py input.pdf --output chunks.jsonl --contextualize

What this pack covers

• Installation and environment setup
• Single-document conversion
• CLI usage and key flags
• Advanced PDF pipeline configuration
• Batch conversion and export
• Hybrid chunking for RAG pipelines
• Common troubleshooting notes
• DOCX / OOXML interoperability fallbacks for Word-native edge cases

DOCX / OOXML interoperability and boundaries

Compared with Anthropic's docx skill, the biggest gap was not generic format conversion but Word-native workflows around legacy .doc, tracked changes, raw OOXML inspection, and page-preview rendering.

This docling skill now includes a lightweight supplement for those cases:

• convert legacy .doc files to .docx or PDF with LibreOffice
• on WSL, if Linux soffice is unavailable but Windows Microsoft Word is installed, the helper can use Word automation as a conversion fallback
• extract .docx content with tracked changes visible via pandoc --track-changes=...
• unpack / repack .docx/.pptx/.xlsx archives for raw XML inspection
• render Word documents to page-preview JPEGs via PDF + pdftoppm

Still intentionally out of scope:

• full-fidelity Word document authoring with docx-js
• deep OOXML schema validation / redline surgery / comment editing
• replacing a dedicated Word production workflow when the user needs polished .docx deliverables rather than extraction

Rule of thumb:

• if the goal is extraction / structured export / chunking, start with Docling
• if the goal is Word-native inspection or preprocessing before Docling, use the DOCX interoperability helper next

Task index

Start with the smallest task that answers your need. In Hermes, use the mirrored files under references/ for direct access via skill_view:

• references/install.md - installation, optional extras, offline models
• references/convert.md - one-document conversion and export
• references/cli.md - official CLI patterns and important flags
• references/advanced_pdf.md - OCR, tables, remote services, model artifacts, streams
• references/batch.md - convert_all, partial failures, batch export
• references/chunk.md - HybridChunker, tokenizer choice, contextualized text
• references/troubleshooting.md - common failure modes and operational notes
• references/docx-interoperability.md - legacy .doc, tracked changes, OOXML unpack/repack, and preview-image fallbacks

The original imported tasks/ directory is still preserved on disk.

Package map

• SKILL.md - overview and routing
• manifest.txt - machine-readable file list
• requirements.txt - suggested Python dependencies
• references/*.md - Hermes-accessible task docs mirrored from the imported tasks/ directory
• references/docx-interoperability.md - docx-specific supplement inspired by comparison with Anthropic's docx skill

Original imported task docs preserved on disk:

• tasks/install.md
• tasks/convert.md
• tasks/cli.md
• tasks/advanced_pdf.md
• tasks/batch.md
• tasks/chunk.md
• tasks/troubleshooting.md

Scripts:

• scripts/docling_common.py - shared helpers for import checks, converter setup, and export
• scripts/docling_convert.py - convert one source and export multiple formats
• scripts/docling_batch.py - batch conversion with summary JSON
• scripts/docling_chunk.py - convert + chunk to JSONL
• scripts/docling_docx_tools.py - LibreOffice / pandoc / OOXML ZIP helpers for Word-native edge cases

Examples:

• examples/basic_convert.py
• examples/custom_pdf_pipeline.py
• examples/multi_format_convert.py
• examples/hybrid_chunking.py
• examples/offline_models.py
• examples/stream_convert.py

Practical defaults

• Start with DocumentConverter() unless you need explicit PDF tuning.
• For PDFs with tables, enable table structure and keep TableFormerMode.ACCURATE unless speed matters more than quality.
• For OCR-heavy scans, explicitly turn OCR on and select an OCR backend only after you have installed its dependencies.
• For large corpora, export at least Markdown and JSON.
• For RAG, keep both raw chunk text and contextualized chunk text.

Deliverable expectations

• Clear installation path for CPU-only and standard environments
• Explicit handling for local/offline artifacts
• Multiple export formats (md, json, html, text, doctags, optional yaml)
• Batch summaries that distinguish success, partial success, and failure
• No hidden reliance on remote services