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-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-extraction-fallback)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-extraction-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-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 | $0.00018 | $0.01091 |
| Opus 5 | $0.00009 | $0.00545 |
| Sonnet 5 | $0.00004 | $0.00218 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
pdf-extraction-fallback scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Extraction Fallback Strategy
When processing documents (especially PDFs), initial extraction attempts may fail due to formatting, encryption, or tool limitations. This skill provides a systematic fallback approach that tries multiple extraction methods before declaring failure.
Core Principle
Never declare completion after a single tool failure. Instead, iterate through a hierarchy of extraction methods, each with different capabilities and limitations.
Fallback Hierarchy
Attempt extraction methods in this order:
Stage 1: Direct PDF Reading
Try native PDF libraries first (fastest, preserves structure):
import PyPDF2
from pypdf import PdfReader
def extract_with_pypdf(pdf_path):
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text() or ""
return text
Stage 2: Shell-based Extraction (pdftotext)
If Stage 1 fails, use system tools:
# Install if needed: apt-get install poppler-utils
pdftotext -layout input.pdf output.txt
pdftotext -raw input.pdf output.txt # Alternative layout
import subprocess
def extract_with_pdftotext(pdf_path):
result = subprocess.run(
['pdftotext', '-layout', pdf_path, '-'],
capture_output=True, text=True
)
if result.returncode == 0:
return result.stdout
raise Exception("pdftotext failed")
Stage 3: Alternative Python Parsers
Try different Python libraries with varying capabilities:
# pdfplumber - better for tables
import pdfplumber
def extract_with_pdfplumber(pdf_path):
text = ""
with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
text += page.extract_text() or ""
return text
# pdfminer - handles complex layouts
from pdfminer.high_level import extract_text
def extract_with_pdfminer(pdf_path):
return extract_text(pdf_path)
Stage 4: OCR Fallback
For scanned images or when text extraction fails:
# Using tesseract
convert input.pdf output-%d.png # Convert to images first
tesseract output-0.png result --psm 6
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 · 155 lines · 18 tokens per session scan A 40cf6390489b
pdf-extraction-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 18 tokens to every session and 1,091 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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