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 reliable-pdf-extraction-ac5f89git 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/reliable-pdf-extraction-ac5f89)<a href="https://agentmods.dev/skills/hkuds/openspace/reliable-pdf-extraction-ac5f89"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/reliable-pdf-extraction-ac5f89/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openspace/reliable-pdf-extraction-ac5f89"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/reliable-pdf-extraction-ac5f89.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00027 | $0.00723 |
| Opus 5 | $0.00014 | $0.00362 |
| Sonnet 5 | $0.00005 | $0.00145 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
reliable-pdf-extraction-ac5f89 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 5d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reliable PDF Text Extraction
Problem
The read_file tool with filetype='pdf' can be unreliable for PDF text extraction. It may:
- Return binary image data instead of text
- Fail with errors on certain PDF structures
- Lose formatting or structured content
Solution
Use run_shell with dedicated PDF extraction tools instead of relying on read_file for PDFs.
Methods
Method 1: pdftotext (Recommended)
pdftotext input.pdf output.txt
Or to extract to stdout:
pdftotext input.pdf -
With layout preservation:
pdftotext -layout input.pdf output.txt
Method 2: pdfinfo (Metadata)
pdfinfo input.pdf
Useful for checking page count, dimensions, and PDF properties before extraction.
Method 3: Python with PyMuPDF (fitz)
import fitz # PyMuPDF
doc = fitz.open("input.pdf")
text = ""
for page in doc:
text += page.get_text()
doc.close()
Method 4: Python with pdfplumber (Tables)
import pdfplumber
with pdfplumber.open("input.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
tables = page.extract_tables()
Workflow
-
Check PDF exists and is readable:
pdfinfo input.pdf 2>/dev/null || echo "PDF not accessible" -
Extract text using pdftotext:
pdftotext -layout input.pdf - > extracted_text.txt -
If pdftotext fails, try Python fallback:
import fitz doc = fitz.open("input.pdf") for i, page in enumerate(doc): print(f"--- Page {i+1} ---") print(page.get_text()) doc.close() -
Verify extraction succeeded:
- Check output is non-empty
- Verify text is readable (not binary/garbled)
- Confirm expected content is present
When to Use
| Tool | Best For |
|---|---|
pdftotext |
Fast, simple text extraction |
pdftotext -layout |
Preserving spacing/formatting |
PyMuPDF |
Complex PDFs, programmatic access |
pdfplumber |
Tables and structured data |
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.
- 5d ago First seen · 121 lines · 27 tokens per session scan A c0b0a2227ffd
reliable-pdf-extraction-ac5f89 is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 27d ago), licensed MIT. It adds 27 tokens to every session and 723 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
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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…