About docling
A document-conversion skill for developers and users who need to read or process varied files. It describes CLI, Python SDK, structured extraction, chunking, and service-based conversion to Markdown or JSON.
Key features
- 01Convert diverse document formats
- 02Export Markdown or structured JSON
- 03Use CLI or Python SDK
- 04Extract typed fields and chunk documents
- 05Send conversion to a service
Use cases
- 01Read a PDF or URL from the shell
- 02Batch-convert documents with custom pipelines
- 03Prepare document chunks for retrieval
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/doclingin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/cli.mdextraction.mdpython-sdk.mdrag.mdservice-client.mdslim-packaging.mdDetails
From the project READMEDocling
Docling converts documents — PDF, DOCX, PPTX, XLSX, HTML, Markdown, AsciiDoc,
CSV, images, audio, and XML — into a single unified representation, the
DoclingDocument, which you can export as Markdown (human-readable) or
JSON (structured, lossless). Reach for Docling whenever you need to
understand the content of a file you cannot read directly, especially PDFs
(including scanned ones, via OCR or a vision-language model).
The fastest thing that works: the CLI
If you just need to read a document's content, run the CLI. It is installed with
the docling package and accepts a local path or a URL:
docling report.pdf --to md --output /tmp/ # → /tmp/report.md
docling https://example.com/paper.pdf --to json --output /tmp/
Output files are named after the input (report.pdf → report.md). Default
output directory is the current directory. This handles the majority of
"what's in this file" requests. See references/cli.md
for pipelines (standard vs VLM vs native), OCR engines, tables, scanned PDFs,
passwords, and every flag.
Choosing how to use Docling
| You need to… | Use | Reference |
|---|---|---|
| Read / convert a file once, from the shell | CLI (docling …) | references/cli.md |
| Convert programmatically, tune the pipeline, batch, ASR, export images/tables | Python SDK (DocumentConverter + PipelineOptions) | references/python-sdk.md |
| Pull specific typed fields out of a document (not the whole doc) | DocumentExtractor (structured extraction, beta) | references/extraction.md |
| Chunk documents for retrieval / feed a RAG index | Chunking + framework loaders | references/rag.md |
| Offload conversion to a remote service — low latency, scalable, no local ML deps or GPU | Service Client (self-hosted or managed docling-serve) | references/service-client.md |
| Install only the dependencies you actually use | docling-slim extras | references/slim-packaging.md |
Rules of thumb:
- Local, one-off, no code → CLI.
- Custom pipeline, chunking, structure analysis, embedding in an app → Python SDK.
- Many documents, low-latency, no GPU/ML install to manage, scale on demand
→ Service Client against a
docling-serveendpoint (self-hosted or the managed Docling for IBM watsonx service). - Minimize install size / avoid pulling torch and OCR engines you don't need → docling-slim with targeted extras.
Running without installing (uvx)
You can run the CLI without a persistent install:
uvx --from docling docling report.pdf --to md --output /tmp/
Output conventions
- Always report the conversion status and (for PDFs) the page count.
- If the user does not specify a format, ask whether they want Markdown (readable) or JSON / DoclingDocument (structured, lossless).
- For tables, prefer
export_to_markdown()/export_to_dataframe()on the table item (Python) — see references/python-sdk.md. - If a converted PDF comes back near-empty, repeated, or full of
�, the source is likely scanned or complex layout — retry with OCR or--pipeline vlm(see references/cli.md).