xingfanxiaxingfanxia/AX-skills/mineru-pdf-parser/SKILL.md

mineru-pdf-parser

Skill4 starsChanged 54 days agoMITView on GitHub

Parse complex PDFs (and images / DOCX / PPTX / XLSX) into LLM-ready Markdown + structured JSON with MinerU — tables, formulas, and reading order preserved. Use for research ingestion, RAG data prep, and academic papers / technical reports where layout fidelity matters. Triggers: "PDF解析", "PDF转Markdown", "提取PDF表格/公式", "parse this PDF", "MinerU". NOT for reading a few pages (just read them), or for merge/split/rotate/fill/OCR-in-place — use the `pdf` skill for those.

Installs packages

2 other projects use a version of this.

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Fission21/MiJitheir own version, first seen Sep 24, 2026
vibe-coding-era/skillstheir own version, first seen Jul 2, 2026

Other versions2 projects use a version of this.
Word for word · 0
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Wrote their own version · 2
Fission21/MiJi★ 11same name
vibe-coding-era/skills★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
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Common
Distinctive
Very specific

Very specific:
Before declaring done: confirm {name}.md + {name}_content_list.json exist (report actual paths), and spot-check 1–2 pages against the …
line 67

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.

Files

SKILL.md

MinerU PDF Parser

Wraps the MinerU CLI (PyPI mineru, v3.4.0 verified 2026-07). CLI-only — there is no stable from mineru import MinerU Python API; drive it via the mineru command.

Why MinerU over the alternatives: PyMuPDF4LLM is AGPL and Marker is GPL — both license blockers for most codebases. MarkItDown's pdfminer.six backend loses heading levels (~0% retention on structured docs). Docling (MIT, ~3s/page on CPU) is the legitimate fallback if MinerU install fails.

Install

uv pip install -U "mineru[core]"        # persistent install (Python 3.10–3.13, macOS 14+)
uvx --from "mineru[core]" mineru --help # ad-hoc, no install (~106 pkgs resolved on first run)

Usage

mineru -p <input> -o <output_dir>          # file OR directory; pdf/image/docx/pptx/xlsx
mineru -p paper.pdf -o out -b pipeline     # no GPU → pipeline (pure-CPU safe)
mineru -p big.pdf -o out -s 0 -e 49        # page range (0-based) for large files

Backend selection (`-b`, default `hybrid-engine`)

Backend	Trait	Pick when
`pipeline`	Most general, runs pure-CPU	No GPU, simple/text PDFs, OCR langs via `-l`
`hybrid-engine`	Default; high accuracy, local compute	GPU/Apple-Silicon box; `--effort high` adds image/chart analysis
`vlm-engine`	VLM high accuracy, local compute	GPU box, formula/table-dense papers
`vlm-http-client` / `hybrid-http-client`	Offload to OpenAI-compatible server via `-u <url>`	Remote vLLM/SGLang/LMDeploy deployment

Other flags: -m auto|txt|ocr (pipeline/hybrid only), -f/-t formula/table toggles (default on), --effort medium|high (hybrid only; medium skips image/chart analysis).

First run — model download

Models auto-download from HuggingFace on first parse (large; looks like a hang — tell the user). If HF is blocked: export MINERU_MODEL_SOURCE=modelscope. Pre-fetch with mineru-models-download -s auto -m all. CUDA/MPS acceleration is auto-attempted on Linux/macOS.

Output (per input file, under the output dir)

{name}.md — the LLM-ready Markdown
{name}_content_list.json — structured blocks in reading order (RAG-friendly)
{name}_middle.json — intermediate detail (debugging)
{name}_layout.pdf (+ {name}_span.pdf, pipeline only) — visual QC of layout/reading order
exact file set varies by backend; see output docs

Pitfalls

Pitfall	Handling
First run "hangs"	It's the model download — pre-announce; pre-fetch with `mineru-models-download`
No GPU, default backend crawls	Force `-b pipeline` (the only pure-CPU-safe backend)
Scanned PDFs	Quality tracks scan clarity; retry once with another backend, then report honestly — don't silently deliver garbage
Hundreds of pages	Memory pressure — parse in `-s/-e` ranges or split the file first
Heading-swallow	A section title rendered as emphasized text inside a table cell isn't detected as a layout heading — consecutive identical `##` headers appear with no separating context. Recover by cross-referencing an anchor value (account number, opening balance, figure caption) against the source page

Before declaring done: confirm {name}.md + {name}_content_list.json exist (report actual paths), and spot-check 1–2 pages against the original for lost/garbled tables and formulas. Also check:

Compression ratio — a text-born PDF should compress ~5-10x into Markdown; a scan should barely shrink. A ratio outside that range means OCR silently no-op'd or the parse collapsed.
Dedupe — if two differently-named PDFs produce byte-identical or equal-length Markdown, md5 the source files before reporting — MinerU does not dedupe inputs.

Mandatory human review (don't deliver unchecked): complex math (LaTeX export is lossy), archival needs that preserve page numbers/headers/footers (parsers strip them), and legal/financial/medical documents (sample back-check against the source PDF is required, not optional).

<!-- Pitfall rows adapted from staruhub/ClaudeSkills (MIT); all CLI facts verified against `mineru --help` v3.4.0 + https://opendatalab.github.io/MinerU/ -->

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