Gemini Document Processing

What it does

When your agent uses it

• Processing documents
• Extracting structured data
• Summarizing PDFs
• Answering questions about document content

Example prompts

• “/gemini-document-processing”

Requirements

• Python 3
• A Bash shell
• A credential in GEMINI_API_KEY

Workflow steps

7 steps, taken from the step headings in SKILL.md.

1. 1API Key Configuration
2. 2Install Dependencies
3. 3Extract Structured Data from PDF
4. 4Summarize Long Document
5. 5Answer Questions About Document
6. 6Process with Python SDK
7. 7Structured Output with JSON Schema

What it can do on your machine

Read from SKILL.md and the folder at commit c6c0686. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 2 files in scripts/ (Shell and Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • aistudio.google.com
  • ai.google.dev

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • GEMINI_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gemini Document Processing loads about 1.6k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 401 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~89

When it runs · the whole SKILL.md, loaded when a task matches

~1.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:36

  2. `.env` file in skill directory (`.claude/skills/gemini-document-processing/.env`)

• NoteMentions a .env fileSKILL.md:37

  3. `.env` file in project root

• NoteMentions a .env fileSKILL.md:49

  cho "GEMINI_API_KEY=your-api-key-here" > .env

• NoteMentions a .env fileSKILL.md:54

  cho "GEMINI_API_KEY=your-api-key-here" > .env

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from einverne/dotfiles at commit c6c0686, republished under its GPL-3.0 licence (© einverne). 401 words, ~1,627 tokens.

name

gemini-document-processing

description

Guide for implementing Google Gemini API document processing - analyze PDFs with native vision to extract text, images, diagrams, charts, and tables. Use when processing documents, extracting structured data, summarizing PDFs, answering questions about document content, or converting documents to structured formats. (project)

export GEMINI_API_KEY="your-api-key-here"

cd .claude/skills/gemini-document-processing
echo "GEMINI_API_KEY=your-api-key-here" > .env

echo "GEMINI_API_KEY=your-api-key-here" > .env

pip install google-genai python-dotenv

# Use the provided script
python .claude/skills/gemini-document-processing/scripts/process-document.py \
  --file invoice.pdf \
  --prompt "Extract invoice details as JSON" \
  --format json

# Process and summarize
python .claude/skills/gemini-document-processing/scripts/process-document.py \
  --file report.pdf \
  --prompt "Provide a concise executive summary"

# Q&A on document content
python .claude/skills/gemini-document-processing/scripts/process-document.py \
  --file contract.pdf \
  --prompt "What are the key terms and conditions?"

from google import genai

client = genai.Client()

# Read PDF
with open('document.pdf', 'rb') as f:
    pdf_data = f.read()

# Process document
response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=[
        'Extract key information from this document',
        genai.types.Part.from_bytes(
            data=pdf_data,
            mime_type='application/pdf'
        )
    ]
)

print(response.text)

from google import genai
from pydantic import BaseModel

class InvoiceData(BaseModel):
    invoice_number: str
    date: str
    total: float
    vendor: str

client = genai.Client()

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=[
        'Extract invoice details',
        genai.types.Part.from_bytes(
            data=open('invoice.pdf', 'rb').read(),
            mime_type='application/pdf'
        )
    ],
    config=genai.types.GenerateContentConfig(
        response_mime_type='application/json',
        response_schema=InvoiceData
    )
)

invoice_data = InvoiceData.model_validate_json(response.text)

PDF < 20MB?
├─ Yes → Use inline base64 encoding
└─ No  → Use File API

Need structured JSON output?
├─ Yes → Define response_schema with Pydantic
└─ No  → Get text response

Multiple queries on same PDF?
├─ Yes → Use File API + Context Caching
└─ No  → Inline encoding is sufficient

# Basic usage
python scripts/process-document.py --file document.pdf --prompt "Your prompt"

# With JSON output
python scripts/process-document.py --file document.pdf --prompt "Extract data" --format json

# With File API (for large files)
python scripts/process-document.py --file large-document.pdf --prompt "Summarize" --use-file-api

# Multiple prompts
python scripts/process-document.py --file document.pdf --prompt "Question 1" --prompt "Question 2"

# Check API key is set
./scripts/check-api-key.sh

© einverne, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in claude/skills/gemini-document-processing of einverne/dotfiles.

• SKILL.md
• .env.example
• CREATION_SUMMARY.md
• README.md
• references/code-examples.md
• references/gemini-document-processing-report.md
• references/quick-reference.md
• scripts/check-api-key.sh
• scripts/process-document.py

Open the folder on GitHubat commit c6c0686