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 DavidROliverBA/ArchitectKB --skill pdf-to-pagegit clone --depth 1 https://github.com/DavidROliverBA/ArchitectKBWrote 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/davidroliverba/architectkb/pdf-to-page)<a href="https://agentmods.dev/skills/davidroliverba/architectkb/pdf-to-page"><img src="https://agentmods.dev/badge/skills/davidroliverba/architectkb/pdf-to-page/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/davidroliverba/architectkb/pdf-to-page"><img src="https://agentmods.dev/badge/skills/davidroliverba/architectkb/pdf-to-page.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.03147 |
| Opus 5 | $0.00000 | $0.01573 |
| Sonnet 5 | $0.00000 | $0.00629 |
| Haiku 4.5 | $0.00000 | $0.00315 |
Grade A, and why
pdf-to-page 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 11d 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 — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pdf-to-page
Convert PDF documents into Page notes with extracted images saved as PNG files in +Attachments/.
Updated: 2026-01-08 - Now uses docling for faster, more accurate PDF processing with native table recognition and reading order detection. User can choose between Sonnet (concise) or Opus (comprehensive) analysis.
Usage
/pdf-to-page <pdf-path>
/pdf-to-page +Attachments/architecture-spec.pdf
/pdf-to-page +Attachments/meeting-presentation.pdf --title "Custom Title"
Instructions
This skill uses docling for PDF structure extraction + 1 agent for visual analysis and YourOrg entity extraction.
Phase 1: PDF Loading & Validation
- Verify the PDF file exists at the specified path
- Check file extension is
.pdf - Note any custom title provided by user (otherwise derive from filename)
- Prepare output directory:
+Attachments/for PNG files
Phase 1.5: Analysis Depth Selection
Use AskUserQuestion to ask the user which analysis depth they prefer:
Question: "What level of analysis do you want for this PDF?"
Header: "Analysis"
Options:
1. "Sonnet - Concise" (Recommended)
Description: "Faster processing, concise overview. Best for straightforward documents."
2. "Opus - Comprehensive"
Description: "Deeper analysis with detailed YourOrg context. Best for complex technical documents."
When to recommend each:
- Sonnet: Default choice. Fast, cost-effective, good for most documents
- Opus: Use for complex technical documents, architecture specs, or when comprehensive YourOrg entity extraction is critical
Store the selection for use in Phase 3.
Phase 2: Docling Processing (Fast & Accurate)
Use docling to extract PDF structure with native table recognition and reading order detection:
from docling.document_converter import DocumentConverter
import json
# Initialize converter
converter = DocumentConverter()
# Process PDF
result = converter.convert(pdf_path)
# Extract structured outputs
markdown_content = result.document.export_to_markdown()
doc_dict = result.document.export_to_dict()
json_data = json.dumps(doc_dict, indent=2)
# Get statistics
page_count = len(result.document.pages)
table_count = len(result.document.tables) if hasattr(result.document, 'tables') else 0
image_count = len(result.document.images) if hasattr(result.document, 'images') else 0
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.
- 11d ago First seen · 428 lines · 0 tokens per session scan A adf1c192390c
pdf-to-page is a skill published in the GitHub repository DavidROliverBA/ArchitectKB (52 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,147 tokens. 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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