Agent skill

Grobid PDF Parsing

by wentorai in wentorai/research-plugins

Extract structured text, metadata, and references from academic PDFs

MITAuto-check passedDocuments & Office

Install Grobid PDF Parsing

skills CLI
$ npx skills add wentorai/research-plugins --skill grobid-pdf-parsing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins grobid-pdf-parsing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tools/document/grobid-pdf-parsing .claude/skills/grobid-pdf-parsing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
grobid-pdf-parsing
GitHub stars
298
Used in
1 other repo
Token cost
~2.5k tokens
SKILL.md length
329 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Extract structured text, metadata, and references from academic PDFs

  • Tasks that involve PDF
  • SKILL.md covers Overview, Installation, REST API Usage and Parsing TEI XML Output, plus 3 more sections
  • Calls docker, curl and git; reaches github.com and tei-c.org
  • Tasks that involve REST APIs

What it does

Grobid PDF Parsing is an agent skill from wentorai/research-plugins. Extract structured text, metadata, and references from academic PDFs

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering PDF and REST APIs. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve PDF
  • Tasks that involve REST APIs

Example prompts

  • “/grobid-pdf-parsing”

Requirements

  • Python 3
  • Docker

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • docker
    • curl
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • tei-c.org
    • w3.org

    Also links to:

    • grobid.readthedocs.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Grobid PDF Parsing loads about 2.5k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 329 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 329 words, ~2,501 tokens.

Download SKILL.mdSave it as .claude/skills/grobid-pdf-parsing/SKILL.md (or your agent's skills folder).
name
grobid-pdf-parsing
description
Extract structured text, metadata, and references from academic PDFs

GROBID PDF Parsing Guide

Overview

Academic PDFs are the primary format for distributing research, yet extracting structured data from them remains challenging. PDFs encode visual layout, not semantic structure -- headings, paragraphs, equations, tables, and citations are all just positioned text and graphics. GROBID (GeneRation Of BIbliographic Data) is the leading open-source tool for parsing academic PDFs into structured XML/TEI format, extracting metadata, body text, references, and figures with high accuracy.

GROBID is used by major academic platforms including CORE, ResearchGate, and others for large-scale document processing. It combines machine learning models (CRF and deep learning) with heuristic rules to handle the diverse formatting of academic papers across publishers and disciplines.

This guide covers installing and running GROBID, using its REST API for batch processing, extracting specific elements (metadata, references, body sections), and integrating GROBID output into downstream workflows such as knowledge bases, systematic reviews, and literature analysis pipelines.

Installation

bash
# Pull the latest GROBID image
docker pull grobid/grobid:0.8.1

# Run GROBID server
docker run --rm --init \
  --ulimit core=0 \
  -p 8070:8070 \
  grobid/grobid:0.8.1

# GROBID is now running at http://localhost:8070
# Web console: http://localhost:8070/console
From Source
bash
git clone https://github.com/kermitt2/grobid.git
cd grobid
./gradlew clean install
./gradlew run

REST API Usage

Process Full Document
bash
# Process a single PDF and get TEI XML
curl -v --form input=@paper.pdf \
  http://localhost:8070/api/processFulltextDocument \
  -o paper.tei.xml

# With options
curl -v --form input=@paper.pdf \
  --form consolidateHeader=1 \
  --form consolidateCitations=1 \
  --form includeRawCitations=1 \
  http://localhost:8070/api/processFulltextDocument \
  -o paper.tei.xml
API Endpoints
EndpointPurposeInputOutput
/api/processFulltextDocumentFull paper parsingPDFTEI XML
/api/processHeaderDocumentMetadata onlyPDFTEI XML (header)
/api/processReferencesReference parsingPDFTEI XML (refs)
/api/processCitationParse citation stringTextTEI XML
/api/processDateParse date stringTextStructured date
Python Client
python
import requests
from pathlib import Path

class GrobidClient:
    def __init__(self, base_url='http://localhost:8070'):
        self.base_url = base_url

    def process_fulltext(self, pdf_path, consolidate_header=True,
                         consolidate_citations=True):
        """Process a PDF and return TEI XML."""
        url = f'{self.base_url}/api/processFulltextDocument'
        files = {'input': open(pdf_path, 'rb')}
        data = {
            'consolidateHeader': '1' if consolidate_header else '0',
            'consolidateCitations': '1' if consolidate_citations else '0',
        }
        response = requests.post(url, files=files, data=data)
        response.raise_for_status()
        return response.text

    def process_header(self, pdf_path):
        """Extract only header metadata from PDF."""
        url = f'{self.base_url}/api/processHeaderDocument'
        files = {'input': open(pdf_path, 'rb')}
        response = requests.post(url, files=files)
        response.raise_for_status()
        return response.text

    def is_alive(self):
        """Check if GROBID server is running."""
        try:
            resp = requests.get(f'{self.base_url}/api/isalive')
            return resp.status_code == 200
        except requests.ConnectionError:
            return False

# Usage
client = GrobidClient()
if client.is_alive():
    tei_xml = client.process_fulltext('paper.pdf')
    with open('paper.tei.xml', 'w') as f:
        f.write(tei_xml)

Parsing TEI XML Output

Extracting Metadata
python
from lxml import etree

def parse_tei_metadata(tei_xml):
    """Extract title, authors, abstract from TEI XML."""
    ns = {'tei': 'http://www.tei-c.org/ns/1.0'}
    root = etree.fromstring(tei_xml.encode('utf-8'))

    # Title
    title_el = root.find('.//tei:titleStmt/tei:title', ns)
    title = title_el.text if title_el is not None else ''

    # Authors
    authors = []
    for author in root.findall('.//tei:sourceDesc//tei:author', ns):
        forename = author.findtext('.//tei:forename', '', ns)
        surname = author.findtext('.//tei:surname', '', ns)
        if surname:
            authors.append(f'{forename} {surname}'.strip())

    # Abstract
    abstract_el = root.find('.//tei:profileDesc/tei:abstract', ns)
    abstract = ''.join(abstract_el.itertext()).strip() if abstract_el is not None else ''

    # DOI
    doi_el = root.find('.//tei:idno[@type="DOI"]', ns)
    doi = doi_el.text if doi_el is not None else ''

    return {
        'title': title,
        'authors': authors,
        'abstract': abstract,
        'doi': doi,
    }
Extracting Body Sections
python
def parse_tei_sections(tei_xml):
    """Extract structured sections from TEI XML body."""
    ns = {'tei': 'http://www.tei-c.org/ns/1.0'}
    root = etree.fromstring(tei_xml.encode('utf-8'))

    sections = []
    for div in root.findall('.//tei:body/tei:div', ns):
        head = div.findtext('tei:head', '', ns).strip()
        paragraphs = []
        for p in div.findall('tei:p', ns):
            text = ''.join(p.itertext()).strip()
            if text:
                paragraphs.append(text)
        sections.append({
            'heading': head,
            'n': div.get('n', ''),
            'paragraphs': paragraphs,
        })

    return sections
Extracting References
python
def parse_tei_references(tei_xml):
    """Extract structured references from TEI XML."""
    ns = {'tei': 'http://www.tei-c.org/ns/1.0'}
    root = etree.fromstring(tei_xml.encode('utf-8'))

    refs = []
    for bib in root.findall('.//tei:listBibl/tei:biblStruct', ns):
        ref = {'id': bib.get('{http://www.w3.org/XML/1998/namespace}id', '')}

        # Title
        title_el = bib.find('.//tei:title[@level="a"]', ns)
        if title_el is None:
            title_el = bib.find('.//tei:title', ns)
        ref['title'] = title_el.text if title_el is not None else ''

        # Authors
        ref['authors'] = []
        for author in bib.findall('.//tei:author', ns):
            name = f"{author.findtext('.//tei:forename', '', ns)} {author.findtext('.//tei:surname', '', ns)}".strip()
            if name:
                ref['authors'].append(name)

        # Year
        date_el = bib.find('.//tei:date[@type="published"]', ns)
        ref['year'] = date_el.get('when', '') if date_el is not None else ''

        # DOI
        doi_el = bib.find('.//tei:idno[@type="DOI"]', ns)
        ref['doi'] = doi_el.text if doi_el is not None else ''

        refs.append(ref)

    return refs

Batch Processing

Processing a Directory of PDFs
python
from pathlib import Path
import json
from concurrent.futures import ThreadPoolExecutor

def batch_process(pdf_dir, output_dir, max_workers=4):
    """Process all PDFs in a directory using GROBID."""
    client = GrobidClient()
    pdf_dir = Path(pdf_dir)
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    pdf_files = list(pdf_dir.glob('*.pdf'))
    print(f"Processing {len(pdf_files)} PDFs...")

    def process_one(pdf_path):
        try:
            tei = client.process_fulltext(str(pdf_path))
            meta = parse_tei_metadata(tei)
            refs = parse_tei_references(tei)

            # Save TEI XML
            tei_path = output_dir / f'{pdf_path.stem}.tei.xml'
            tei_path.write_text(tei)

            # Save structured JSON
            json_path = output_dir / f'{pdf_path.stem}.json'
            json_path.write_text(json.dumps({
                'metadata': meta,
                'references': refs,
                'n_references': len(refs),
            }, indent=2))

            return pdf_path.name, 'success'
        except Exception as e:
            return pdf_path.name, f'error: {str(e)}'

    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(executor.map(process_one, pdf_files))

    for name, status in results:
        print(f"  {name}: {status}")

batch_process('papers/', 'parsed_output/')

Best Practices

  • Use consolidation flags. consolidateHeader=1 and consolidateCitations=1 cross-reference against Crossref for better metadata.
  • Handle errors gracefully. Some PDFs are scanned images, corrupted, or have unusual layouts. Always wrap processing in try/except.
  • Limit concurrent requests. GROBID is CPU-intensive. 4-8 concurrent requests is usually optimal.
  • Validate output. Spot-check a sample of parsed documents against the original PDFs.
  • Use GROBID for structured extraction, not OCR. For scanned documents, run OCR first (Tesseract) then GROBID.
  • Keep GROBID updated. Each release improves parsing accuracy, especially for newer publisher formats.

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/tools/document/grobid-pdf-parsing of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Paper Interpretationdigoal/blog8.6k—~1.5kAutomated safety check: PassGPL-2.0
Paper2slidesQuZhan51496/paper2anything450—~3.8kAutomated safety check: NotesApache-2.0

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Questions about Grobid PDF Parsing

What does Grobid PDF Parsing do?

Extract structured text, metadata, and references from academic PDFs. Grobid PDF Parsing is an agent skill from wentorai/research-plugins.

When should I use Grobid PDF Parsing?

Grobid PDF Parsing fits situations like: tasks that involve PDF; tasks that involve REST APIs.

How do I install Grobid PDF Parsing in Claude Code?

Run `npx skills add wentorai/research-plugins --skill grobid-pdf-parsing -a claude-code`. Or copy the skill folder (skills/tools/document/grobid-pdf-parsing in wentorai/research-plugins) into .claude/skills/grobid-pdf-parsing in your project. Claude Code loads it when a task matches its description.

How do I install Grobid PDF Parsing in Codex?

Run `npx skills add wentorai/research-plugins --skill grobid-pdf-parsing -a codex`. Or copy the skill folder (skills/tools/document/grobid-pdf-parsing in wentorai/research-plugins) into .agents/skills/grobid-pdf-parsing in your project. Codex loads it when a task matches its description.

Can I use Grobid PDF Parsing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill grobid-pdf-parsing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/grobid-pdf-parsing, .gemini/skills/grobid-pdf-parsing, .github/skills/grobid-pdf-parsing and .opencode/skills/grobid-pdf-parsing in your project.

What does Grobid PDF Parsing need to run?

Going by SKILL.md and its folder, Grobid PDF Parsing needs the command-line tools its instructions call (docker, curl and git). Our summary lists: Python 3; Docker.

Does Grobid PDF Parsing access the network?

SKILL.md names 4 domains. In commands or code: github.com, tei-c.org and w3.org; the agent is likely to contact these when it follows the instructions. As links in the text: grobid.readthedocs.io. This is read from the text; nothing was executed.

Is Grobid PDF Parsing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Grobid PDF Parsing use?

Grobid PDF Parsing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Grobid PDF Parsing use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Grobid PDF Parsing?

Skills that share tags, products or a category with Grobid PDF Parsing: Pullmd (AeternaLabsHQ/pullmd, 486 stars), PDF (zai-org/ZCode, 7.7k stars), Split PDF (scunning1975/MixtapeTools, 474 stars) and Paper Interpretation (digoal/blog, 8.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Grobid PDF Parsing?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.