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Chat with PDF — Claude Code skill: ask questions, get summaries

A skill that lets your agent answer questions about a PDF, summarize it, and pull out specific data. Useful for contracts, reports and papers.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add claude-office-skills/skills --skill chat-with-pdf -a claude-code
Open on GitHub

What Chat with PDF does

Four modes cover most PDF work. Factual questions about contract value, parties or key dates. Summaries from three bullets up to an executive summary. Extraction of names, titles, financial figures and action items. Analysis of risks, ambiguous terms and obligations.

Answers come back structured: the answer itself, a source such as page and section, a quote from the document, and a confidence level. Tables get their own format with item, value and location columns. You can also point the skill at several PDFs at once and get a comparison table of differences and similarities.

It runs through the office-mcp MCP server and its extract_text_from_pdf and get_pdf_metadata tools, so that server has to be running first. Scanned files go through OCR, and quality tracks scan quality; handwriting is a weak spot. Password-protected PDFs need the password, and anything past 500 pages may need chunking. Charts are described in words rather than read as numbers.

Narrow questions pay off. Asking what section 5.2 says about liability beats asking for a general rundown, and on long documents section-by-section beats one broad summary request.

What it helps with

  • pull the value and term out of a contract without reading all of it
  • turn a financial report into a table of metrics
  • compare termination terms across two contracts side by side
  • locate the limitations section in a research paper
  • produce a three-bullet executive summary for a manager

Example prompts for the agent

  • What type of contract is this and who are the parties?
  • Extract all amounts and dates into a table with page numbers
  • Compare the termination conditions in Contract A and Contract B

How to install Chat with PDF

1Skills CLI (Vercel)

npx skills add claude-office-skills/skills --skill chat-with-pdf -a claude-code

2Manually: put the files in place

~/.claude/skills/chat-with-pdf/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/chat-with-pdf/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about Chat with PDF

How do I install Chat with PDF in Claude?

Run npx skills add claude-office-skills/skills --skill chat-with-pdf -a claude-code. The skill file then sits at ~/.claude/skills/chat-with-pdf/SKILL.md.

What does Chat with PDF do?

It answers questions about PDF content, produces summaries at different lengths, and extracts specific data such as names, amounts and action items. Answers include a page reference and a quote.

What does the skill need to work?

The office-mcp MCP server with the extract_text_from_pdf and get_pdf_metadata tools. Without it the skill cannot read the document text.

Source text

SKILL.md · MIT
---
# ═══════════════════════════════════════════════════════════════════════════════
# CLAUDE OFFICE SKILL - Enhanced Metadata v2.0
# ═══════════════════════════════════════════════════════════════════════════════

# Basic Information
name: Chat with PDF
description: "Answer questions about PDF content, summarize, and extract information"
version: "1.0"
author: claude-office-skills
license: MIT

# Categorization
category: pdf
tags:
  - pdf
  - chat
  - qa
  - extraction
department: All

# AI Model Compatibility
models:
  recommended:
    - claude-sonnet-4
    - claude-opus-4
  compatible:
    - claude-3-5-sonnet
    - gpt-4
    - gpt-4o

# MCP Tools Integration
mcp:
  server: office-mcp
  tools:
    - extract_text_from_pdf
    - get_pdf_metadata

# Skill Capabilities
capabilities:
  - pdf_qa
  - content_extraction
  - summarization

# Language Support
languages:
  - en
  - zh
---

# Chat with PDF

Have intelligent conversations about PDF documents - ask questions, get summaries, and extract specific information.

## Overview

This skill enables you to:
- Ask questions about PDF content
- Get summaries at various detail levels
- Extract specific data points
- Compare information across multiple PDFs
- Find relevant sections quickly

## How to Use

### Basic Interaction
1. Share or upload the PDF document
2. Ask your question or request
3. Get contextual answers with citations

### Question Types

**Factual Questions**
```
"What is the contract value mentioned in this document?"
"Who are the parties involved in this agreement?"
"What are the key dates mentioned?"
```

**Summarization**
```
"Summarize this document in 3 bullet points"
"Give me an executive summary"
"What are the main topics covered?"
```

**Extraction**
```
"Extract all names and titles mentioned"
"List all financial figures in the document"
"Find all action items or deliverables"
```

**Analysis**
```
"What are the risks mentioned in this contract?"
"Are there any ambiguous terms?"
"What obligations does Party A have?"
```

## Output Formats

### Q&A Format
```markdown
**Question**: [Your question]

**Answer**: [Direct answer to your question]

**Source**: Page [X], Section [Y]
> "[Relevant quote from document]"

**Confidence**: [High/Medium/Low]
```

### Summary Format
```markdown
## Document Summary

**Type**: [Contract/Report/Manual/etc.]
**Pages**: [X]
**Date**: [If mentioned]

### Key Points
1. [Main point 1]
2. [Main point 2]
3. [Main point 3]

### Important Details
- [Detail 1]
- [Detail 2]
```

### Extraction Format
```markdown
## Extracted Information

### [Category 1]
| Item | Value | Location |
|------|-------|----------|
| [Item 1] | [Value] | Page X |
| [Item 2] | [Value] | Page Y |

### [Category 2]
...
```

## Best Practices

### For Better Answers
1. **Be specific**: "What is the termination clause?" vs "Tell me about the contract"
2. **Reference sections**: "What does Section 5.2 say about liability?"
3. **Ask follow-ups**: Build on previous answers for deeper understanding

### For Better Extraction
1. **Specify format**: "Extract as a table" or "List as bullet points"
2. **Name the fields**: "Extract: name, date, amount, description"
3. **Set criteria**: "Only extract amounts over $10,000"

### For Better Summaries
1. **Specify length**: "Summarize in 100 words" or "3 bullet points"
2. **Focus area**: "Summarize the financial terms only"
3. **Audience**: "Summarize for a legal team" vs "for executives"

## Example Workflows

### Contract Review
```
1. "What type of contract is this?"
2. "Who are the parties and what are their roles?"
3. "What are the key obligations for each party?"
4. "What is the term and renewal process?"
5. "What are the termination conditions?"
6. "Are there any unusual or concerning clauses?"
```

### Research Paper Analysis
```
1. "What is the main thesis or hypothesis?"
2. "What methodology was used?"
3. "What are the key findings?"
4. "What are the limitations mentioned?"
5. "What future research do they suggest?"
```

### Financial Report
```
1. "What is the total revenue reported?"
2. "How does this compare to last year?"
3. "What are the main expense categories?"
4. "What guidance is given for next quarter?"
5. "Extract all financial metrics into a table"
```

## Multi-Document Support

When working with multiple PDFs:

```
"Compare the terms in Contract A vs Contract B"
"Which document mentions [topic]?"
"Create a summary table comparing key points across all documents"
```

### Comparison Output
```markdown
## Document Comparison

| Aspect | Document A | Document B |
|--------|------------|------------|
| Term Length | 2 years | 3 years |
| Value | $50,000 | $75,000 |
| Termination | 30 days notice | 60 days notice |

### Key Differences
1. [Difference 1]
2. [Difference 2]

### Similarities
1. [Similarity 1]
2. [Similarity 2]
```

## Handling Challenges

### Scanned PDFs (Image-based)
- OCR will be applied automatically
- Quality depends on scan quality
- May have recognition errors

### Complex Layouts
- Tables may need reformatting
- Multi-column text is processed left-to-right
- Footnotes processed separately

### Long Documents
- Ask about specific sections for accuracy
- Request page-by-page summaries for overview
- Use targeted questions over broad ones

## Limitations

- Cannot execute code embedded in PDFs
- Password-protected PDFs need password
- Very large PDFs (500+ pages) may need chunking
- Handwritten content recognition is limited
- Cannot guarantee 100% accuracy on scanned documents
- Charts and images are described, not analyzed numerically

## Privacy Note

Document contents are processed according to the AI provider's privacy policy. For sensitive documents, consider:
- Using on-premise solutions
- Redacting sensitive information first
- Checking data retention policies

© claude-office-skills · MIT · claude-office-skills/skills

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