pdftotext-fallback

pdftotext-fallback is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 25 tokens per session (774 once invoked), scanned A, original, MIT.

A fallback method for extracting readable text from PDF files when the usual file reader or Python tools return binary, image, or garbled data. It uses pdftotext, a command-line utility for reading PDF text.

In plain words
What is it for?
Use it to extract PDF text to the command line or capture it in an agent response as a recovery step.
Why use it?
It provides another way to recover text when standard PDF extraction fails because of encoding, format, or tool limitations.

Skill for Claude CodeCodex

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

Good fit Use it to extract PDF text to the command line or capture it in an agent response as a recovery step.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/pdftotext-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 pdftotext-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 pdftotext-fallback

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/pdftotext-fallback.svg)](https://agentmods.dev/skills/hkuds/openspace/pdftotext-fallback)
Your own site
<a href="https://agentmods.dev/skills/hkuds/openspace/pdftotext-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdftotext-fallback.svg" alt="Measured on agentmods" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 774 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00025 $0.00774
Opus 5 $0.00013 $0.00387
Sonnet 5 $0.00005 $0.00155
Haiku 4.5 $0.00003 $0.00077

Measured 3d ago against content hash 5d2ce16592e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

pdftotext-fallback scanned grade A with 0 findings 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

benchmarks/gdpval/skills/pdftotext-fallback/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

pdftotext Fallback for PDF Text Extraction

When to Use

Apply this pattern when:

  • read_file on a PDF returns binary/image data instead of readable text
  • Python-based PDF extraction (PyMuPDF, pdfplumber, etc.) in execute_code_sandbox fails or returns garbled content
  • You need reliable text extraction from PDF files as a recovery strategy

Why This Works

The pdftotext utility (from poppler-utils) is a mature, command-line tool that handles many PDF edge cases that confuse Python libraries or the read_file tool. It's pre-installed on most Linux systems and provides consistent, reliable text extraction.

Steps

Step 1: Detect the Failure

Recognize extraction failure when:

  • read_file returns binary content, image data, or garbled text
  • Error messages indicate encoding issues or unsupported formats
  • Python PDF libraries in sandbox fail with import errors or extraction failures

Step 2: Use pdftotext via run_shell

Extract to stdout (recommended for quick extraction):

pdftotext /path/to/file.pdf -

The - argument outputs directly to stdout for easy capture in your tool response.

Example:

run_shell("pdftotext document.pdf -")

Step 3: Handle Complex PDFs

Preserve layout (maintains original formatting):

pdftotext -layout /path/to/file.pdf -

Extract to a file (for large PDFs):

pdftotext /path/to/file.pdf /path/to/output.txt

Then read the output file with read_file.

Handle encoded text:

pdftotext -enc UTF-8 /path/to/file.pdf -

Step 4: Verify and Continue

  • Check the extracted text for completeness
  • If text is still garbled, the PDF may be image-based (scanned) - consider OCR tools
  • Proceed with your task using the extracted text

Code Examples

Basic extraction:

# Simple text extraction
text = run_shell("pdftotext document.pdf -")

With error handling:

# Try extraction, check for success
result = run_shell("pdftotext document.pdf - && echo 'SUCCESS' || echo 'FAILED'")

Read the full file on GitHub · 107 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 · 107 lines · 25 tokens per session scan A 5d2ce16592e8

Subscribe to this mod's changes

pdftotext-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 25 tokens to every session and 774 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

pydicom

Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.

K-Dense-AI/scientific-agent-skills · 56 tokens

foundry-hosted-agent-validation

Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.

microsoft/agent-framework · 82 tokens

skill-doc-delivery

Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.

nyldn/claude-octopus · 29 tokens

parse-document

Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.

superlinked/sie · 64 tokens

meta-web-to-pdf-briefing

Render a topic into a distributable PDF briefing in three steps: web search → bullet summary → styled PDF. Trigger when the user asks for a PDF briefing on a single topic.

opensquilla/opensquilla · 45 tokens

chat-complex-documents

Chat with and search your complex documents — ask questions, extract tables and fields, and get answers grounded in the source. Connects the hosted Unstructured Transform MCP server to parse, structure, and enrich PDFs, Word/Excel/PowerPoint, images, scanned files, emails, and 60+ other formats into clean, AI-ready…

vellum-ai/vellum-assistant · 90 tokens