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-extractiongit 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)<a href="https://agentmods.dev/skills/hkuds/openspace/reliable-pdf-extraction"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/reliable-pdf-extraction.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00661 |
| Opus 5 | $0.00012 | $0.00331 |
| Sonnet 5 | $0.00005 | $0.00132 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
reliable-pdf-extraction 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 4d 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 — 102 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' often returns binary image data, errors, or unusable output when attempting to extract text from PDF documents. This makes it unreliable for structured data extraction tasks.
Solution
Use run_shell with command-line tools (pdftotext, pdfinfo) or execute_code_sandbox with Python libraries (PyMuPDF, pdfplumber) to extract PDF text content reliably.
Methods
Method 1: pdftotext (Recommended for simple extraction)
# Extract all text to stdout
pdftotext input.pdf -
# Or extract to file
pdftotext input.pdf output.txt
cat output.txt
Method 2: pdfinfo (For metadata)
pdfinfo input.pdf
Method 3: Python with PyMuPDF (fitz)
import fitz # PyMuPDF
doc = fitz.open("input.pdf")
text = ""
for page in doc:
text += page.get_text()
print(text)
doc.close()
Method 4: Python with pdfplumber (Better for tables/structured data)
import pdfplumber
with pdfplumber.open("input.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
print(text)
# For tables:
# tables = page.extract_tables()
Workflow
-
Attempt
read_filewithfiletype='pdf'first (in case it works) -
Check output - If you receive:
- Binary/garbage data
- Error messages
- Empty or truncated content
- Image data instead of text
-
Fall back to one of the extraction methods above:
- Use
pdftotextviarun_shellfor quick text extraction - Use
pdfplumberviaexecute_code_sandboxfor structured data/tables - Use
PyMuPDFfor complex layouts or when you need more control
- Use
-
Process the extracted text for your task
Example Usage
# Via run_shell
result = run_shell(command="pdftotext document.pdf -")
pdf_text = result.stdout
# Via execute_code_sandbox
code = """
import pdfplumber
with pdfplumber.open("/path/to/document.pdf") as pdf:
for page in pdf.pages:
print(page.extract_text())
"""
result = execute_code_sandbox(code=code)
pdf_text = result.stdout
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.
- 4d ago First seen · 102 lines · 23 tokens per session scan A 6763808cb5cd
reliable-pdf-extraction is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 26d ago), licensed MIT. It adds 23 tokens to every session and 661 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.
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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.
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chat-complex-documents
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