orthogonal-pdf-processor
Process PDFs - extract text, tables, and structured data from documents
Best use case
orthogonal-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 orthogonal-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 orthogonal-pdf-processor Compares
| Feature / Agent | orthogonal-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
## Setup
Read your credentials from ~/.gooseworks/credentials.json:
```bash
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")
```
If ~/.gooseworks/credentials.json does not exist, tell the user to run: `npx gooseworks login`
All endpoints use Bearer auth: `-H "Authorization: Bearer $GOOSEWORKS_API_KEY"`
Extract text, tables, and structured data from PDF documents.
## Workflow
### Step 1: Fetch PDF Content
Use Linkup to fetch PDF URLs:
```bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"linkup","path":"/fetch","body":{"url":"https://example.com/document.pdf"}}'
```
### Step 2: Extract with AI
Use ScrapeGraph to extract specific content:
```bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
"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
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"riveter","path":"/v1/run"}'
"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
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"scrapegraph","path":"/v1/markdownify","body":{"website_url":"https://example.com/document.pdf"}}'
```
## Example Usage
```bash
# Extract data from financial report
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
"website_url": "https://example.com/annual-report.pdf",
"user_prompt": "Extract revenue, profit, and key business metrics with their values"
}'
# Extract invoice data
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"riveter","path":"/v1/run"}'
"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
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/search \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt":"linkup API endpoints"}' api show riveter
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/search \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt":"scrapegraph API endpoints"}'
Example: `curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/details \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
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