pdf-processor
Process PDFs - extract text, tables, and structured data from documents
Best use case
pdf-processor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process PDFs - extract text, tables, and structured data from documents
Teams using pdf-processor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/orthogonal-pdf-processor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pdf-processor Compares
| Feature / Agent | pdf-processor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process PDFs - extract text, tables, and structured data from documents
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# PDF Processor - Extract Data from PDFs
Extract text, tables, and structured data from PDF documents.
## Workflow
### Step 1: Fetch PDF Content
Use Linkup to fetch PDF URLs:
```bash
orth api run linkup /fetch --body '{"url": "https://example.com/document.pdf"}'
```
### Step 2: Extract with AI
Use ScrapeGraph to extract specific content:
```bash
orth api run scrapegraph /v1/smartscraper --body '{
"website_url": "https://example.com/report.pdf",
"user_prompt": "Extract all financial figures, tables, and key metrics from this document"
}'
```
### Step 3: Extract Tables
Get structured table data:
```bash
orth api run riveter /v1/run --body '{
"input": {
"urls": ["https://example.com/report.pdf"]
},
"output": {
"tables": {"prompt": "Extract all tables with titles, headers, and rows", "contexts": ["urls"]}
}
}'
```
### Step 4: Convert to Markdown
Get readable markdown output:
```bash
orth api run scrapegraph /v1/markdownify --body '{"website_url": "https://example.com/document.pdf"}'
```
## Example Usage
```bash
# Extract data from financial report
orth api run scrapegraph /v1/smartscraper --body '{
"website_url": "https://example.com/annual-report.pdf",
"user_prompt": "Extract revenue, profit, and key business metrics with their values"
}'
# Extract invoice data
orth api run riveter /v1/run --body '{
"input": {"urls": ["https://example.com/invoice.pdf"]},
"output": {
"vendor": {"prompt": "Vendor name", "contexts": ["urls"]},
"amount": {"prompt": "Total amount", "contexts": ["urls"]},
"date": {"prompt": "Invoice date", "contexts": ["urls"]}
}
}'
```
## Tips
- Specify exact data you need for better extraction
- Use schemas for consistent structured output
- Handle multi-page documents in chunks
- Verify extracted numbers against source
## Discover More
List all endpoints, or add a path for parameter details:
```bash
orth api show linkup
orth api show riveter
orth api show scrapegraph
```
Example: `orth api show olostep /v1/scrapes` for endpoint parameters.Related Skills
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