pdf-extraction-fallback

pdf-extraction-fallback is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 18 tokens per session (1,091 once invoked), scanned A, original, MIT.

A fallback strategy for extracting text from PDFs with different parsing methods and shell tools.

In plain words
What is it for?
It helps extract text from PDFs when direct reading does not work.
Why use it?
One extraction method may fail because of formatting, encryption, or tool limitations. Trying alternatives reduces the chance of stopping after the first failure.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps extract text from PDFs when direct reading does not work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/pdf-extraction-fallback
About the project

OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.

HKUDS/OpenSpace · 7,534 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add HKUDS/OpenSpace --skill pdf-extraction-fallback
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pdf-extraction-fallback

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/pdf-extraction-fallback.svg)](https://agentmods.dev/skills/hkuds/openspace/pdf-extraction-fallback)
Your own site
<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-extraction-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-extraction-fallback.svg" alt="Measured on agentmods" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,091 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00018 $0.01091
Opus 5 $0.00009 $0.00545
Sonnet 5 $0.00004 $0.00218
Haiku 4.5 $0.00002 $0.00109

Measured 3d ago against content hash 40cf6390489b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

pdf-extraction-fallback scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

result = subprocess.run(
benchmarks/gdpval/skills/pdf-extraction-fallback/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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):

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:

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

# 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:

# Using tesseract
convert input.pdf output-%d.png  # Convert to images first
tesseract output-0.png result --psm 6

Read the full file on GitHub · 155 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 155 lines · 18 tokens per session scan A 40cf6390489b

Subscribe to this mod's changes

pdf-extraction-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 18 tokens to every session and 1,091 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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