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name: policy-document-parser description: Extracts and structures company policy information from PDF documents, particularly travel expense policies with destination-specific caps. Reads policy PDFs, extracts structured data about expense limits by location and employee level, and converts policy tables into machine-readable formats for automated validation.

Policy Document Parser

When to Use

Use this skill when you need to:

• Analyze company policy documents (PDF format)
• Extract structured expense limits from travel policy tables
• Parse destination-specific caps by employee level
• Convert policy tables into machine-readable formats (JSON/CSV)
• Prepare policy data for automated expense validation systems

Core Workflow

1. Initial Setup & Document Discovery

• Scan the workspace for policy documents (typically PDF files)
• Identify the main policy document (look for files like policy_en.pdf, travel_policy.pdf, etc.)
• Use filesystem-list_directory to explore the workspace structure

2. Policy Document Analysis

• Read the PDF document using pdf-tools-read_pdf_pages
• Extract the full text content for analysis
• Identify the document structure and locate policy tables

3. Table Extraction & Parsing

• Focus on sections containing destination-specific expense caps
• Look for tables with the following structure:
  • Destination cities/countries
  • Employee levels (L1, L2, L3, L4, etc.)
  • Category caps (Accommodation, Meals, Transportation, Communication, Miscellaneous)
  • Per-day or per-trip limits

4. Data Structuring

• Convert extracted tables into structured JSON format
• Organize data by: city → employee_level → category → limit
• Include global rules (receipt thresholds, airfare policies, etc.) as separate metadata

5. Output Generation

• Create machine-readable policy files (JSON preferred)
• Generate summary reports of extracted policy limits
• Prepare data for integration with expense validation systems

Key Patterns from Trajectory

Document Structure Recognition

The policy PDF typically contains:

1. Header with policy name, effective date, currency
2. Global rules section (airfare, receipt thresholds, exceptions)
3. Destination-specific tables with caps by employee level
4. Multiple pages with consistent table formatting

Data Extraction Strategy

• Extract all pages first to understand full document scope
• Look for patterns like "Destination-Specific Caps" headers
• Parse tables with city names followed by level-based caps
• Capture per-day vs per-trip limits (Transportation vs Communication/Miscellaneous)

Error Handling

• Handle missing or malformed tables gracefully
• Validate extracted data against expected structure
• Log parsing issues for manual review

Output Formats

Primary Output: Structured Policy JSON