Agent skill

Robust PDF Extraction

by HKUDS in HKUDS/OpenSpace

Multi-method PDF extraction with sequential fallback and OCR for scanned documents

MITAuto-check passedDocuments & Office

Install Robust PDF Extraction

skills CLI
$ npx skills add HKUDS/OpenSpace --skill robust-pdf-extraction -a claude-code

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

GitHub CLI
$ gh skill install HKUDS/OpenSpace robust-pdf-extraction --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/HKUDS/OpenSpace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/benchmarks/gdpval/skills/robust-pdf-extraction .claude/skills/robust-pdf-extraction && 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
robust-pdf-extraction
GitHub stars
7.7k
Token cost
~1.3k tokens
SKILL.md length
281 words
Files
2
Skills in repo
199
Repo updated
First seen
Licence
MIT

At a glance

Multi-method PDF extraction with sequential fallback and OCR for scanned documents

  • Works in 6 steps: Verify File Accessibility → Attempt Primary Extraction (pdfplumber) → Fallback to Secondary Method (pypdfium2) → …
  • Tasks that involve PDF
  • SKILL.md covers When to Use, Workflow Steps, Complete Workflow Function and Dependencies, plus 2 more sections
  • Calls pdftotext and pip

What it does

Robust PDF Extraction is an agent skill from HKUDS/OpenSpace. Multi-method PDF extraction with sequential fallback and OCR for scanned documents

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Documents & Office, covering PDF. It works with pypdf. The repository describes itself as: "OpenSpace: The Skill Management Layer for AI Agents" -- https://open-space.cloud/. The licence is MIT.

When your agent uses it

  • Tasks that involve PDF

Example prompts

  • “/robust-pdf-extraction”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Verify File Accessibility
  2. Attempt Primary Extraction (pdfplumber)
  3. Fallback to Secondary Method (pypdfium2)
  4. Fallback to Tertiary Method (pdftotext)
  5. Detect Scanned/Image-Based PDFs
  6. OCR Fallback for Scanned Documents

What it can do on your machine

Read from SKILL.md and the folder at commit 3827781. 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:

    • pdftotext
    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Robust PDF Extraction loads about 1.3k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 281 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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 HKUDS/OpenSpace at commit 3827781, republished under its MIT licence (© HKUDS). 281 words, ~1,285 tokens.

Download SKILL.mdSave it as .claude/skills/robust-pdf-extraction/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
robust-pdf-extraction
description
Multi-method PDF extraction with sequential fallback and OCR for scanned documents

Robust PDF Extraction Workflow

This skill provides a systematic approach to extracting text from PDF files, handling both text-based and scanned/image-based documents through progressive fallback methods.

When to Use

  • Processing PDFs of unknown or mixed types (text vs. scanned images)
  • Critical document processing where extraction failure is not acceptable
  • Batch processing multiple PDFs with varying formats

Workflow Steps

Step 1: Verify File Accessibility

Before attempting extraction, confirm the PDF exists and is readable:

bash
# Check file exists and get basic info
ls -la /path/to/document.pdf

# Or search for files if location uncertain
find /path -name "*.pdf" -type f 2>/dev/null
Step 2: Attempt Primary Extraction (pdfplumber)

Start with pdfplumber for best text structure preservation:

python
import pdfplumber

def extract_with_pdfplumber(pdf_path):
    text = ""
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            page_text = page.extract_text()
            if page_text:
                text += page_text + "\n"
    return text.strip()
Step 3: Fallback to Secondary Method (pypdfium2)

If pdfplumber returns empty or incomplete text:

python
import pdfium2

def extract_with_pypdfium2(pdf_path):
    pdf = pdfium2.PdfDocument(pdf_path)
    text = ""
    for page in pdf:
        text_page = page.get_textpage()
        page_text = text_page.get_text_bounded()
        if page_text:
            text += page_text + "\n"
    return text.strip()
Step 4: Fallback to Tertiary Method (pdftotext)

If pypdfium2 also fails, use command-line pdftotext:

bash
pdftotext /path/to/document.pdf - 2>/dev/null

Or in Python:

python
import subprocess

def extract_with_pdftotext(pdf_path):
    result = subprocess.run(
        ['pdftotext', pdf_path, '-'],
        capture_output=True,
        text=True
    )
    return result.stdout.strip()
Step 5: Detect Scanned/Image-Based PDFs

After each extraction attempt, verify text was actually extracted:

python
def is_meaningful_text(text, min_chars=50):
    """Check if extracted text is meaningful (not empty or just whitespace)"""
    if not text:
        return False
    # Remove whitespace and check length
    cleaned = ''.join(text.split())
    return len(cleaned) >= min_chars
Step 6: OCR Fallback for Scanned Documents

If all text extraction methods return empty/insufficient text, the PDF is likely scanned. Use OCR:

python
import pdf2image
import pytesseract
from PIL import Image

def extract_with_ocr(pdf_path, dpi=300):
    """Extract text from scanned PDFs using OCR"""
    text = ""
    images = pdf2image.convert_from_path(pdf_path, dpi=dpi)
    for image in images:
        page_text = pytesseract.image_to_string(image)
        text += page_text + "\n"
    return text.strip()

Complete Workflow Function

python
def robust_pdf_extract(pdf_path):
    """
    Extract text from PDF using progressive fallback methods.
    Returns (text, method_used) tuple.
    """
    methods = [
        ("pdfplumber", extract_with_pdfplumber),
        ("pypdfium2", extract_with_pypdfium2),
        ("pdftotext", extract_with_pdftotext),
    ]
    
    for method_name, extract_func in methods:
        try:
            text = extract_func(pdf_path)
            if is_meaningful_text(text):
                return text, method_name
        except Exception as e:
            print(f"{method_name} failed: {e}")
            continue
    
    # All text methods failed - try OCR
    try:
        text = extract_with_ocr(pdf_path)
        if is_meaningful_text(text):
            return text, "ocr"
    except Exception as e:
        print(f"OCR failed: {e}")
    
    return "", "failed"

Dependencies

Install required packages:

bash
pip install pdfplumber pypdfium2 pdf2image pytesseract pillow
# Also need system packages:
# apt-get install poppler-utils tesseract-ocr  # Debian/Ubuntu
# brew install poppler tesseract  # macOS

Best Practices

  1. Log which method succeeded - helps identify document types for future optimization
  2. Set reasonable character thresholds - adjust min_chars based on expected document content
  3. Handle exceptions gracefully - each method may fail for different reasons
  4. Consider DPI for OCR - higher DPI (300+) improves accuracy but increases processing time
  5. Cache results - if processing same PDFs repeatedly, store successful extraction method per file

Troubleshooting

SymptomLikely CauseSolution
All methods return emptyScanned PDFOCR fallback should handle this
pdfplumber fails with permission errorFile locked or permissions issueCheck file permissions with ls -la
OCR returns gibberishLow quality scan or wrong languageIncrease DPI, specify language in pytesseract
pdftotext not foundMissing poppler-utilsInstall system package

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

Files

SKILL.md and 1 other file in benchmarks/gdpval/skills/robust-pdf-extraction of HKUDS/OpenSpace.

  • SKILL.md
  • .skill_id

Open the folder on GitHubat commit 3827781

Compare with similar skills

Robust PDF Extraction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Robust PDF Extraction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Robust PDF Extraction this skillHKUDS/OpenSpace7.7k—~1.3kAutomated safety check: PassMIT
PDFnuoyimanaituling/manus-x830—~985Automated safety check: PassNone
PDFeinverne/dotfiles12147 repos~1.8kAutomated safety check: PassProprietary
Reportlabjimmc414/Kosmos5941 repos~4.2kAutomated safety check: PassNone
PDFguyi-a/pi-ling106—~3.3kAutomated safety check: PassMIT
PDF ReadingWide-Moat/open-computer-use1261 repos~2.7kAutomated safety check: PassProprietary

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Works with

Questions about Robust PDF Extraction

What does Robust PDF Extraction do?

Multi-method PDF extraction with sequential fallback and OCR for scanned documents. Robust PDF Extraction is an agent skill from HKUDS/OpenSpace.

When should I use Robust PDF Extraction?

Robust PDF Extraction fits situations like: tasks that involve PDF.

How do I install Robust PDF Extraction in Claude Code?

Run `npx skills add HKUDS/OpenSpace --skill robust-pdf-extraction -a claude-code`. Or copy the skill folder (benchmarks/gdpval/skills/robust-pdf-extraction in HKUDS/OpenSpace) into .claude/skills/robust-pdf-extraction in your project. Claude Code loads it when a task matches its description.

How do I install Robust PDF Extraction in Codex?

Run `npx skills add HKUDS/OpenSpace --skill robust-pdf-extraction -a codex`. Or copy the skill folder (benchmarks/gdpval/skills/robust-pdf-extraction in HKUDS/OpenSpace) into .agents/skills/robust-pdf-extraction in your project. Codex loads it when a task matches its description.

Can I use Robust PDF Extraction 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 HKUDS/OpenSpace --skill robust-pdf-extraction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/robust-pdf-extraction, .gemini/skills/robust-pdf-extraction, .github/skills/robust-pdf-extraction and .opencode/skills/robust-pdf-extraction in your project.

What does Robust PDF Extraction need to run?

Going by SKILL.md and its folder, Robust PDF Extraction needs the command-line tools its instructions call (pdftotext and pip). Our summary lists: Python 3.

Does Robust PDF Extraction access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Robust PDF Extraction 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 Robust PDF Extraction use?

Robust PDF Extraction 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 Robust PDF Extraction use?

About 1.3k tokens (SKILL.md is roughly 5.1k 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 Robust PDF Extraction?

Skills that share tags, products or a category with Robust PDF Extraction: PDF (nuoyimanaituling/manus-x, 830 stars), PDF (einverne/dotfiles, 121 stars), Reportlab (jimmc414/Kosmos, 594 stars) and PDF (guyi-a/pi-ling, 106 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Robust PDF Extraction?

HKUDS (a GitHub organization) maintains it in HKUDS/OpenSpace, which has 7,749 GitHub stars. The repository holds 199 skills in this directory. The repository was last updated on August 12, 2026.

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