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-page-verification-correctiongit 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-page-verification-correction)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-page-verification-correction"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-page-verification-correction.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 | $0.01414 |
| Opus 5 | $0.00012 | $0.00707 |
| Sonnet 5 | $0.00005 | $0.00283 |
| Haiku 4.5 | $0.00002 | $0.00141 |
Grade A, and why
pdf-page-verification-correction 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Page Count Verification and Correction
This skill provides a systematic approach to ensure PDFs meet target page count requirements through iterative verification and layout adjustment.
When to Use
- Creating PDFs with strict page limits (e.g., reports, summaries, maps)
- When initial PDF generation may produce variable page counts
- When layout elements (images, tables, text) can cause unpredictable overflow
Prerequisites
- Python with PyPDF2 or fitz (PyMuPDF) installed
- PDF generation capability (ReportLab, matplotlib, etc.)
Workflow Steps
Step 1: Initial PDF Creation
Generate the PDF with your initial layout parameters:
def create_pdf(output_path, params):
"""Create PDF with given layout parameters"""
# Your PDF generation logic here
# params can include: image_size, table_density, font_size, margins
pass
Step 2: Verify Page Count
Check the generated PDF's page count:
import fitz # PyMuPDF
def verify_page_count(pdf_path):
"""Return the number of pages in the PDF"""
doc = fitz.open(pdf_path)
page_count = len(doc)
doc.close()
return page_count
# Alternative with PyPDF2
from PyPDF2 import PdfReader
def verify_page_count_pypdf2(pdf_path):
reader = PdfReader(pdf_path)
return len(reader.pages)
Step 3: Compare Against Target
def check_page_requirement(actual, target_max, target_exact=None):
"""
Check if page count meets requirements
Returns: (meets_requirement, adjustment_needed)
"""
if target_exact is not None:
meets = (actual == target_exact)
direction = "shrink" if actual > target_exact else "expand" if actual < target_exact else None
else:
meets = (actual <= target_max)
direction = "shrink" if actual > target_max else None
return meets, direction
Step 4: Adjust Layout Parameters
If page count exceeds target, adjust one or more parameters:
# Common adjustment strategies
ADJUSTMENT_STRATEGIES = {
'images': {
'action': 'reduce_size',
'param': 'image_scale',
'step': 0.1, # Reduce by 10%
'min': 0.5
},
'tables': {
'action': 'reduce_density',
'param': 'rows_per_page',
'step': 2, # Reduce by 2 rows per page
'min': 5
},
'fonts': {
'action': 'reduce_size',
'param': 'font_size',
'step': 1, # Reduce by 1pt
'min': 8
},
'margins': {
'action': 'reduce',
'param': 'margin_inches',
'step': 0.1, # Reduce by 0.1 inches
'min': 0.3
}
}
def adjust_params(current_params, direction, strategy='images'):
"""Apply adjustment to parameters"""
adjusted = current_params.copy()
strat = ADJUSTMENT_STRATEGIES[strategy]
if direction == 'shrink':
param = strat['param']
current_val = adjusted.get(param, 1.0)
new_val = max(current_val - strat['step'], strat['min'])
adjusted[param] = new_val
return adjusted
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 · 223 lines · 23 tokens per session scan A 39121dc0b1bc
pdf-page-verification-correction is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 23 tokens to every session and 1,414 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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