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 agentmods add skills/hkuds/openspace/local-pdf-extractionnpx skills add HKUDS/OpenSpace --skill local-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/local-pdf-extraction)<a href="https://agentmods.dev/skills/hkuds/openspace/local-pdf-extraction"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/local-pdf-extraction.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 | $0.00023 | $0.00785 |
| Opus 5 | $0.00012 | $0.00392 |
| Sonnet 5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
local-pdf-extraction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local PDF Extraction Workflow
Use this skill when you need to extract text from PDF files that exist locally on the filesystem, and read_file returns binary data instead of readable text.
When to Use
- PDF files exist in the local workspace or known directories
read_fileon PDFs returns binary/garbled data instead of text- You need to process PDF content for analysis, summarization, or data extraction
Step-by-Step Instructions
Step 1: Locate PDF Files
First, list directory contents to find all PDF files:
ls -la *.pdf
# or for recursive search
find . -name "*.pdf" -type f
Step 2: Extract PDFs to Text
Choose one of these methods based on available tools:
Method A: Using pdftotext (poppler-utils)
# Extract single PDF
pdftotext input.pdf output.txt
# Batch extract all PDFs in directory
for pdf in *.pdf; do
pdftotext "$pdf" "${pdf%.pdf}.txt"
done
Method B: Using PyMuPDF (fitz) via Python
python3 << 'EOF'
import fitz # PyMuPDF
import glob
import os
for pdf_path in glob.glob("*.pdf"):
doc = fitz.open(pdf_path)
text = ""
for page in doc:
text += page.get_text()
txt_path = pdf_path.replace(".pdf", ".txt")
with open(txt_path, "w", encoding="utf-8") as f:
f.write(text)
print(f"Extracted: {pdf_path} -> {txt_path}")
EOF
Step 3: Read Extracted Text Files
Once extracted, use read_file to read the .txt files:
# Now you can read the text files normally
content = read_file(filetype="txt", file_path="document.txt")
Step 4: Process Content
Proceed with your analysis, summarization, or data extraction on the text content.
Complete Workflow Example
# Step 1: Find PDFs
ls -la *.pdf
# Step 2: Extract all PDFs to text
for pdf in *.pdf; do
pdftotext "$pdf" "${pdf%.pdf}.txt"
done
# Step 3: Verify extraction
ls -la *.txt
Or as a Python script via run_shell:
python3 << 'SCRIPT'
import fitz, glob
for pdf in glob.glob("*.pdf"):
doc = fitz.open(pdf)
text = "".join(page.get_text() for page in doc)
with open(pdf.replace(".pdf", ".txt"), "w") as f:
f.write(text)
print(f"Done: {pdf}")
SCRIPT
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 · 119 lines · 23 tokens per session scan A 6433b678ee7b
local-pdf-extraction is a skill published in the GitHub repository HKUDS/OpenSpace (7,501 stars, last pushed 23d ago), licensed MIT. It adds 23 tokens to every session and 785 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 2026-08-30.
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