paddlepaddle--paddleocr/docs/version3.x/integrations/skills.en.md

comments

comments
true

PaddleOCR Agent Skills

PaddleOCR provides official Agent Skills that package the routing rules, calling steps, configuration requirements, and best practices for handling results in OCR and document parsing tasks into on-demand modular capabilities, helping Skills-enabled AI apps complete text recognition and layout parsing more reliably.

Choose the Right Skill First

Need	Recommended Skill	Output
Extract plain text from images or PDFs	paddleocr-text-recognition	line-level text with bounding boxes and confidence scores
Preserve headings, paragraphs, tables, formulas, and layout structure	paddleocr-doc-parsing	Markdown / structured output

Included Skills

• paddleocr-text-recognition: extracts text from images, scans, and PDF files.
• paddleocr-doc-parsing: parses complex document layouts and converts them to Markdown or structured output.

Prerequisites

1. Install Python 3.9 or later on the machine that runs the skill.
2. Install PaddleOCR 3.7.0+: pip install "paddleocr>=3.7.0"
3. Get access token from AI Studio

Install into AI Apps

The instructions below cover both skills. Install and configure only the skill or skills you need.

Option 1: Install via skills CLI

The skills CLI installs skills globally on the device so they can be used by supported AI apps. Node.js is required.

npx skills add PaddlePaddle/PaddleOCR -g --skill paddleocr-text-recognition -y
npx skills add PaddlePaddle/PaddleOCR -g --skill paddleocr-doc-parsing -y

This repository is relatively large. On slower networks, npx skills add may time out. If that happens, clone the repository locally first and then install from the local path:

git clone https://github.com/PaddlePaddle/PaddleOCR.git
npx skills add ./PaddleOCR/skills/paddleocr-text-recognition
npx skills add ./PaddleOCR/skills/paddleocr-doc-parsing

Option 2: Install via clawhub (OpenClaw)

clawhub install paddleocr-text-recognition
clawhub install paddleocr-doc-parsing

See the OpenClaw Skills documentation for details.

Option 3: Manual Installation

If the options above do not fit your environment, clone the repository and copy the skill directories to the location required by your AI app:

git clone https://github.com/PaddlePaddle/PaddleOCR.git

The skill source code is located under PaddleOCR/skills. Refer to your AI app documentation to complete the installation:

• Claude Code: https://code.claude.com/docs/en/skills
• claude.ai: https://support.claude.com/en/articles/12512180-use-skills-in-claude
• OpenClaw: https://docs.openclaw.ai/tools/skills

Configure Environment Variables

After installation, configure the following environment variables:

• Required: PADDLEOCR_ACCESS_TOKEN (access token)
• Optional: PADDLEOCR_BASE_URL (API base URL, defaults to official service)

Get access token: visit AI Studio Access Token

Examples for some AI apps:

• Claude Code: add an env field to .claude/settings.local.json in your project:

  {
    "env": {
      "PADDLEOCR_ACCESS_TOKEN": "<ACCESS_TOKEN>"
    }
  }
  

• OpenClaw: add skill configuration to ~/.openclaw/openclaw.json:

  {
    "skills": {
      "entries": {
        "paddleocr-text-recognition": {
          "enabled": true,
          "env": {
            "PADDLEOCR_ACCESS_TOKEN": "<ACCESS_TOKEN>"
          }
        },
        "paddleocr-doc-parsing": {
          "enabled": true,
          "env": {
            "PADDLEOCR_ACCESS_TOKEN": "<ACCESS_TOKEN>"
          }
        }
      }
    }
  }
  

Usage Examples

After configuration, describe the task in natural language and provide a file URL or local path so the AI app can invoke the corresponding skill.

paddleocr-text-recognition

URL example:

Extract all text from this file: https://example.com/invoice.jpg

Local file example:

Extract all text from local file C:\docs\invoice.pdf

paddleocr-doc-parsing

URL example:

Parse this PDF and return the main body plus all tables: https://example.com/report.pdf

Local file example:

Parse local file C:\docs\report.pdf and return complete structured output.