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 document-extractgit 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/document-extract)<a href="https://agentmods.dev/skills/davidroliverba/architectkb/document-extract"><img src="https://agentmods.dev/badge/skills/davidroliverba/architectkb/document-extract/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/document-extract"><img src="https://agentmods.dev/badge/skills/davidroliverba/architectkb/document-extract.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 | $0.00900 |
| Opus 5.5 | $0.00000 | $0.00360 |
| Sonnet 5.5 | $0.00000 | $0.00180 |
| Haiku 4.5 | $0.00000 | $0.00090 |
Grade A, and why
document-extract 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 1mo 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/document-extract
Extract and analyse content from scanned documents, PDFs, and document images using Sonnet sub-agents.
Usage
/document-extract <file-path>
/document-extract +Attachments/meeting-whiteboard.jpg
/document-extract +Attachments/spec-document.pdf
/document-extract +Attachments/handwritten-notes.png --type "meeting notes"
Instructions
This skill uses Sonnet model sub-agents for comprehensive document analysis and text extraction.
Phase 1: Document Loading
- Verify the file exists at the specified path
- Identify document type (PDF, image, photo of document)
- Note any context about document purpose
Phase 2: Comprehensive Analysis (Sonnet Sub-Agents)
Launch these sub-agents using model: "sonnet":
Agent 1: Text Extraction (Sonnet)
Task: Extract all text content from the document
- Read the file using the Read tool
- Perform comprehensive OCR on all visible text
- Preserve structure (headings, paragraphs, lists)
- Handle multiple columns if present
- Extract text from tables maintaining structure
- Note any text that is unclear or uncertain
Return: Complete text extraction with structure preserved
Agent 2: Structure Analysis (Sonnet)
Task: Analyse document structure and formatting
- Read the file
- Identify document type (letter, form, spec, notes, etc.)
- Map section hierarchy
- Identify tables, lists, and special formatting
- Note headers, footers, page numbers
- Identify logos, stamps, signatures
Return: Document structure map
Agent 3: Content Classification (Sonnet)
Task: Classify and categorise the content
- Read the file
- Determine document purpose
- Identify key entities (people, projects, dates, systems)
- Extract action items or tasks
- Find decisions or commitments
- Note any deadlines or dates mentioned
- Identify references to YourOrg projects or systems
Return: Classified content with entity extraction
Agent 4: Quality Assessment (Sonnet)
Task: Assess extraction quality and completeness
- Read the file
- Evaluate image/scan quality
- Identify areas with low confidence extraction
- Note any missing or obscured content
- Assess if re-scan might be needed
- Check for multiple pages
Return: Quality report with confidence scores
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.
- 1mo ago First seen · 165 lines · 0 tokens per session scan A 71ce98662ae6
document-extract is a skill published in the GitHub repository DavidROliverBA/ArchitectKB (53 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 900 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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