---
name: "pdfplumber-table-data-parser"
description: "Extract complex tables, financial statements, and tabular data from PDFs with precision."
metadata:
  author: "jsvine"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "extract table from pdf pdfplumber"
  - "parse financial statement pdf"
  - "convert pdf table to dataframe"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# pdfplumber High-Precision Table Parser

Leverages pdfplumber in Python to inspect visual lines, character coordinates, and table borders, parsing complex financial spreadsheets into structured Pandas DataFrames.

## When to Invoke
Invoke this skill whenever you need to:
- extract table from pdf pdfplumber
- parse financial statement pdf
- convert pdf table to dataframe

## Execution SOP & Rules
1. Open PDF documents within a Python context manager (with pdfplumber.open(path) as pdf).
2. Configure table extraction settings using explicit vertical_strategy and horizontal_strategy options.
3. Clean extracted table cells by stripping whitespace and normalizing empty strings to None.
4. Convert structured table rows directly into Pandas DataFrames or JSON records for downstream use.

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.