pdf-page-verification-correction

pdf-page-verification-correction is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 23 tokens per session (1,414 once invoked), scanned A, original, MIT.

A workflow for checking and correcting the number of pages in a generated PDF.

In plain words
What is it for?
It helps generate a PDF, count its pages with Python tools, and adjust layout settings until the page count is correct.
Why use it?
It helps prevent layouts from exceeding a required page limit because of oversized images, tables, or text.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps generate a PDF, count its pages with Python tools, and adjust layout settings until the page count is correct.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/pdf-page-verification-correction
About the project

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.

HKUDS/OpenSpace · 7,534 stars · on GitHub

Install

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.

Any agent
npx skills add HKUDS/OpenSpace --skill pdf-page-verification-correction
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for pdf-page-verification-correction

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/pdf-page-verification-correction.svg)](https://agentmods.dev/skills/hkuds/openspace/pdf-page-verification-correction)
Your own site
<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>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,414 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 39121dc0b1bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

benchmarks/gdpval/skills/pdf-page-verification-correction/SKILL.md · 223 lines

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

Read the full file on GitHub · 223 lines

Files

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.

Changes

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.

  1. 4d ago First seen · 223 lines · 23 tokens per session scan A 39121dc0b1bc

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

pydicom

Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.

K-Dense-AI/scientific-agent-skills · 56 tokens

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.

microsoft/agent-framework · 82 tokens

skill-doc-delivery

Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.

nyldn/claude-octopus · 29 tokens

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.

superlinked/sie · 64 tokens

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.

opensquilla/opensquilla · 45 tokens

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…

vellum-ai/vellum-assistant · 90 tokens