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Pdf Extraction Fallback

OrganizationPopular
HKUDS
pdf-extraction-fallback

Multi-stage fallback strategy for PDF/document extraction using sequential tool alternatives

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namepdf-extraction-fallback
Stars
7.8K
Forks
923
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Pdf Extraction Fallback AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-extraction-fallback
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/pdf-extraction-fallback .claude/skills/pdf-extraction-fallback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pdf Extraction Fallback in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pdf Extraction Fallback on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pdf Extraction Fallback is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PDF Extraction Fallback Strategy

When processing documents (especially PDFs), initial extraction attempts may fail due to formatting, encryption, or tool limitations. This skill provides a systematic fallback approach that tries multiple extraction methods before declaring failure.

Core Principle

Never declare completion after a single tool failure. Instead, iterate through a hierarchy of extraction methods, each with different capabilities and limitations.

Fallback Hierarchy

Attempt extraction methods in this order:

Stage 1: Direct PDF Reading

Try native PDF libraries first (fastest, preserves structure):

python
import PyPDF2
from pypdf import PdfReader

def extract_with_pypdf(pdf_path):
    reader = PdfReader(pdf_path)
    text = ""
    for page in reader.pages:
        text += page.extract_text() or ""
    return text

Stage 2: Shell-based Extraction (pdftotext)

If Stage 1 fails, use system tools:

bash
# Install if needed: apt-get install poppler-utils
pdftotext -layout input.pdf output.txt
pdftotext -raw input.pdf output.txt  # Alternative layout
python
import subprocess

def extract_with_pdftotext(pdf_path):
    result = subprocess.run(
        ['pdftotext', '-layout', pdf_path, '-'],
        capture_output=True, text=True
    )
    if result.returncode == 0:
        return result.stdout
    raise Exception("pdftotext failed")

Stage 3: Alternative Python Parsers

Try different Python libraries with varying capabilities:

python
# pdfplumber - better for tables
import pdfplumber
def extract_with_pdfplumber(pdf_path):
    text = ""
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            text += page.extract_text() or ""
    return text

# pdfminer - handles complex layouts
from pdfminer.high_level import extract_text
def extract_with_pdfminer(pdf_path):
    return extract_text(pdf_path)

Stage 4: OCR Fallback

For scanned images or when text extraction fails:

bash
# Using tesseract
convert input.pdf output-%d.png  # Convert to images first
tesseract output-0.png result --psm 6
python
# Using pytesseract
from pdf2image import convert_from_path
import pytesseract

def extract_with_ocr(pdf_path):
    images = convert_from_path(pdf_path, dpi=300)
    text = ""
    for image in images:
        text += pytesseract.image_to_string(image)
    return text

Implementation Pattern

python
def robust_pdf_extraction(pdf_path):
    """Try multiple extraction methods until one succeeds."""
    
    extraction_methods = [
        ("PyPDF2", extract_with_pypdf),
        ("pdftotext", extract_with_pdftotext),
        ("pdfplumber", extract_with_pdfplumber),
        ("pdfminer", extract_with_pdfminer),
        ("OCR", extract_with_ocr),
    ]
    
    errors = []
    for method_name, method_func in extraction_methods:
        try:
            print(f"Trying {method_name}...")
            text = method_func(pdf_path)
            if text and text.strip():
                print(f"Success with {method_name}")
                return text
            else:
                errors.append(f"{method_name}: empty result")
        except Exception as e:
            errors.append(f"{method_name}: {str(e)}")
            print(f"{method_name} failed: {e}")
            continue
    
    # All methods failed
    raise Exception(f"All extraction methods failed:\n" + "\n".join(errors))

Success Criteria

A method is considered successful when:

  1. No exceptions are raised during execution
  2. Non-empty text is returned (text.strip() has content)
  3. Content quality meets task requirements (check for expected keywords/patterns)

Best Practices

  1. Log each attempt - Record which methods were tried and why they failed
  2. Validate output - Check extracted text contains expected content markers
  3. Graceful degradation - Proceed with partial data if full extraction isn't possible
  4. Cache successful method - Remember which method worked for similar files
  5. Set timeouts - Prevent OCR or complex parsing from hanging indefinitely

Common Failure Modes

SymptomLikely CauseBest Fallback
Empty pagesImage-based PDFOCR (Stage 4)
Garbled textEncoding issuespdftotext (Stage 2)
Missing tablesSimple parserpdfplumber (Stage 3)
Permission errorsEncrypted PDFCheck password/permissions first
Layout lostComplex formattingpdftotext -layout or pdfplumber

When to Use

  • Processing unknown/untrusted PDF sources
  • Batch processing diverse document types
  • Critical tasks where extraction failure is not acceptable
  • Documents with mixed content (text + images + tables)

Frequently asked questions

What does the Pdf Extraction Fallback AI skill do?

Multi-stage fallback strategy for PDF/document extraction using sequential tool alternatives

Why use Pdf Extraction Fallback on TypingMind?

Because you install it once and use it with any model. Pdf Extraction Fallback is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pdf Extraction Fallback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-extraction-fallback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pdf Extraction Fallback?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pdf Extraction Fallback?

As many as you like. As long as a model supports skills, you can use Pdf Extraction Fallback with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pdf Extraction Fallback AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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