| name | data_processing |
| description | Extract data from PDFs and query CSV files |
Usage
Use this skill when analyzing tabular data (CSV) or unstructured PDF documents within the workspace. This gives you the ability to query dataframes via Pandas and extract raw text (including OCR fallback via PyMuPDF/Tesseract) from PDFs.
[!WARNING]
While extract_pdf_text is useful for quick, ad-hoc reads, do NOT use this to feed multi-agent swarms or complex analyses for files located in the staging/ directory. Those files must be formally ingested into the Knowledge Graph via a rag_ingest pipeline first.
Tools
extract_pdf_text(pdf_path, workspace="default") - Extracts all text from a given PDF file in data_in. Supports fallback OCR.
query_csv(csv_path, query, workspace="default") - Query a CSV file using Pandas. The query can be a standard string like "amount > 100" or a dataframe expression like "df.describe()".
Examples
Action: query_csv
Action Input: {"csv_path": "financials.csv", "query": "df.head(5)"}
Observation: Returns the first 5 rows formatted as a markdown table.
Action: extract_pdf_text
Action Input: {"pdf_path": "architecture_diagram.pdf"}
Observation: Returns the text content extracted from the document.