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Local Pdf Extraction

OrganizationPopular
HKUDS
local-pdf-extraction

Extract text from local PDFs using pdftotext or PyMuPDF via run_shell

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namelocal-pdf-extraction
Stars
7.7K
Forks
918
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 Local Pdf Extraction 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.
  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/local-pdf-extraction .claude/skills/local-pdf-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Local Pdf Extraction 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 Local Pdf Extraction 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 Local Pdf Extraction 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.

Local PDF Extraction Workflow

Use this skill when you need to extract text from PDF files that exist locally on the filesystem, and read_file returns binary data instead of readable text.

When to Use

  • PDF files exist in the local workspace or known directories
  • read_file on PDFs returns binary/garbled data instead of text
  • You need to process PDF content for analysis, summarization, or data extraction

Step-by-Step Instructions

Step 1: Locate PDF Files

First, list directory contents to find all PDF files:

bash
ls -la *.pdf
# or for recursive search
find . -name "*.pdf" -type f

Step 2: Extract PDFs to Text

Choose one of these methods based on available tools:

Method A: Using pdftotext (poppler-utils)
bash
# Extract single PDF
pdftotext input.pdf output.txt

# Batch extract all PDFs in directory
for pdf in *.pdf; do
    pdftotext "$pdf" "${pdf%.pdf}.txt"
done
Method B: Using PyMuPDF (fitz) via Python
bash
python3 << 'EOF'
import fitz  # PyMuPDF
import glob
import os

for pdf_path in glob.glob("*.pdf"):
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text()
    
    txt_path = pdf_path.replace(".pdf", ".txt")
    with open(txt_path, "w", encoding="utf-8") as f:
        f.write(text)
    print(f"Extracted: {pdf_path} -> {txt_path}")
EOF

Step 3: Read Extracted Text Files

Once extracted, use read_file to read the .txt files:

python
# Now you can read the text files normally
content = read_file(filetype="txt", file_path="document.txt")

Step 4: Process Content

Proceed with your analysis, summarization, or data extraction on the text content.

Complete Workflow Example

bash
# Step 1: Find PDFs
ls -la *.pdf

# Step 2: Extract all PDFs to text
for pdf in *.pdf; do
    pdftotext "$pdf" "${pdf%.pdf}.txt"
done

# Step 3: Verify extraction
ls -la *.txt

Or as a Python script via run_shell:

bash
python3 << 'SCRIPT'
import fitz, glob
for pdf in glob.glob("*.pdf"):
    doc = fitz.open(pdf)
    text = "".join(page.get_text() for page in doc)
    with open(pdf.replace(".pdf", ".txt"), "w") as f:
        f.write(text)
    print(f"Done: {pdf}")
SCRIPT

Troubleshooting

  • pdftotext not found: Install with apt-get install poppler-utils or use PyMuPDF method
  • Empty text output: PDF may be image-based; consider OCR tools like pdftoppm + tesseract
  • Encoding issues: Ensure output files use UTF-8 encoding

Key Takeaways

  1. Never use read_file directly on PDFs for text extraction
  2. Always list directory first to confirm PDF locations
  3. Use run_shell with pdftotext or PyMuPDF for reliable extraction
  4. Batch process when multiple PDFs exist
  5. Read the resulting .txt files for further processing

Frequently asked questions

What does the Local Pdf Extraction AI skill do?

Extract text from local PDFs using pdftotext or PyMuPDF via run_shell

Why use Local Pdf Extraction on TypingMind?

Because you install it once and use it with any model. Local Pdf Extraction 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 Local Pdf Extraction in TypingMind?

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

Which AI models can use Local Pdf Extraction?

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 Local Pdf Extraction?

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

Is the Local Pdf Extraction 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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