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.
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.
npx skills add HKUDS/OpenSpace --skill pdf-text-extraction-fallbackgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote 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.
[](https://agentmods.dev/skills/hkuds/openspace/pdf-text-extraction-fallback)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-text-extraction-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-text-extraction-fallback.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00713 |
| Opus 5 | $0.00011 | $0.00357 |
| Sonnet 5 | $0.00004 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
pdf-text-extraction-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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Text Extraction Fallback
When to Use This Skill
Use this skill when read_file with filetype="pdf" returns binary/image data instead of readable text content. This is a common issue with PDF files that contain embedded images or complex formatting.
Steps
1. Validate Parameters First
Before attempting extraction, ensure you're using the correct parameter name:
- Use
filetype(notfile_type) for theread_filefunction - Incorrect parameter names can cause silent failures
# Correct
read_file(filetype="pdf", file_path="document.pdf")
# Incorrect - may fail silently
read_file(file_type="pdf", file_path="document.pdf")
2. Detect Binary Data Issue
After calling read_file, check if the result contains:
- Garbled/binary characters
- Image data representations (e.g.,
b'...'byte strings with non-text content) - Unreadable or corrupted-looking content
If yes, proceed with the pdftotext workaround.
3. Extract Text via pdftotext
Use run_shell to call pdftotext, which extracts text directly from PDF files:
# Extract text to stdout
result = run_shell(command="pdftotext /path/to/document.pdf -")
text_content = result.stdout
The - flag tells pdftotext to output to stdout instead of creating a file.
4. Handle Output and Errors
result = run_shell(command="pdftotext /path/to/document.pdf -")
if result.stderr:
# Check for errors like "pdftotext not found"
# May need to install poppler-utils
pass
text_content = result.stdout
# text_content now contains the extracted text
Example Workflow
# Step 1: Try normal read with correct parameters
content = read_file(filetype="pdf", file_path="reference.pdf")
# Step 2: Check if content is readable
if not content or looks_like_binary(content):
# Step 3: Fall back to pdftotext
result = run_shell(command="pdftotext reference.pdf -")
text_content = result.stdout
# Step 4: Verify extraction succeeded
if result.stderr:
# Handle error (e.g., install pdftotext)
pass
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.
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.
- 3d ago First seen · 103 lines · 22 tokens per session scan A 484d40f0e979
pdf-text-extraction-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 22 tokens to every session and 713 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.
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.
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.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
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.
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.
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…