What is databricks-unstructured-pdf-generation?

A tool for generating synthetic PDF documents and accompanying question-and-evaluation files for RAG systems. RAG, or retrieval-augmented generation, lets an AI retrieve information from documents before answering.

Use it to create sets of described PDF documents for RAG testing, evaluation datasets, demos, and unstructured-data experiments.

It provides test and demonstration documents for checking whether a document-search system finds and uses the right information. The generated files can be stored in Databricks Unity Catalog Volumes.

How alessandro9110/Speech-To-Text-With-Databricks configures Claude Co

Reuse

Borrowing it

Nothing to install: this file belongs to alessandro9110/Speech-To-Text-With-Databricks. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/alessandro9110/Speech-To-Text-With-Databricks/main/.claude/skills/databricks-unstructured-pdf-generation/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/alessandro9110/Speech-To-Text-With-Databricks

Made for: Claude Code.

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README.md

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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.

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Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,831 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.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00039	$0.01831
Opus 5.5	$0.00016	$0.00732
Sonnet 5.5	$0.00008	$0.00366
Haiku 4.5	$0.00004	$0.00183

Measured 1mo ago against content hash a1c26fbf19f8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

databricks-unstructured-pdf-generation 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.

.claude/skills/databricks-unstructured-pdf-generation/SKILL.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Unstructured PDF Generation

Generate realistic synthetic PDF documents using LLM for RAG (Retrieval-Augmented Generation) and unstructured data use cases.

Overview

This skill uses the generate_pdf_documents MCP tool to create professional PDF documents with:

LLM-generated content based on your description
Accompanying JSON files with questions and evaluation guidelines (for RAG testing)
Automatic upload to Unity Catalog Volumes

Quick Start

Use the generate_pdf_documents MCP tool:

catalog: "my_catalog"
schema: "my_schema"
description: "Technical documentation for a cloud infrastructure platform including setup guides, troubleshooting procedures, and API references."
count: 10

This generates 10 PDF documents and saves them to /Volumes/my_catalog/my_schema/raw_data/pdf_documents/ (using default volume and folder).

With Custom Location

Use the generate_pdf_documents MCP tool:

catalog: "my_catalog"
schema: "my_schema"
description: "HR policy documents..."
count: 10
volume: "custom_volume"
folder: "hr_policies"
overwrite_folder: true

Parameters

Parameter	Type	Required	Default	Description
`catalog`	string	Yes	-	Unity Catalog name
`schema`	string	Yes	-	Schema name
`description`	string	Yes	-	Detailed description of what PDFs should contain
`count`	int	Yes	-	Number of PDFs to generate
`volume`	string	No	`raw_data`	Volume name (created if not exists)
`folder`	string	No	`pdf_documents`	Folder within volume for output files
`doc_size`	string	No	`MEDIUM`	Document size: `SMALL` (~1 page), `MEDIUM` (~5 pages), `LARGE` (~10+ pages)
`overwrite_folder`	bool	No	`false`	If true, deletes existing folder contents first

Document Size Guide

SMALL: ~1 page, concise content. Best for quick demos or testing.
MEDIUM: ~4-6 pages, comprehensive coverage. Good balance for most use cases.
LARGE: ~10+ pages, exhaustive documentation. Use for thorough RAG evaluation.

Read the full file on GitHub · 195 lines

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.

1mo ago First seen · 195 lines · 39 tokens per session scan A a1c26fbf19f8

Subscribe to this mod's changes

databricks-unstructured-pdf-generation is a skill published in the GitHub repository alessandro9110/Speech-To-Text-With-Databricks (4 stars, last pushed 8mo ago), licensed MIT. It adds 39 tokens to every session and 1,831 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.

Data and AI Documents PDF RAG and vectors databricks delta-lake generative-ai lakehouse machine speech-to-text

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Speech-To-Text-With-Databricks: Skill for Claude Code