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

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.

Each skill currently supports the following underlying models:

• paddleocr-text-recognition: PP-OCRv5
• paddleocr-doc-parsing: PP-StructureV3, PaddleOCR-VL, PaddleOCR-VL-1.5

Prerequisites

1. Install Python 3.9 or later and uv on the machine that runs the skill.
2. All scripts declare dependencies inline with PEP 723, so uv run resolves them automatically.
3. The skills rely on PaddleOCR official APIs. Visit the PaddleOCR website, click API, choose the model you need, and copy the API_URL and Token.

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:

shellCopy

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 required environment variables so the skills can work correctly:

Examples for some AI apps:

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

  jsonCopy

  {
    "env": {
      "PADDLEOCR_ACCESS_TOKEN": "<ACCESS_TOKEN>",
      "PADDLEOCR_OCR_API_URL": "<OCR_API_URL>",
      "PADDLEOCR_DOC_PARSING_API_URL": "<DOC_PARSING_API_URL>"
    }
  }
  

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

  jsonCopy

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

Local Verification

This section shows how to run smoke tests locally to verify that the skill configuration works as expected.

The examples below cover both skills. If you only need one, run only the corresponding commands.

Before running the commands, make sure your working directory is the directory that contains this document. All scripts declare inline dependencies, so uv resolves them automatically.

1. Configure environment variables as described in Configure Environment Variables.

   shellCopy

   export PADDLEOCR_OCR_API_URL="<OCR_API_URL>"
   export PADDLEOCR_ACCESS_TOKEN="<ACCESS_TOKEN>"
   export PADDLEOCR_DOC_PARSING_API_URL="<DOC_PARSING_API_URL>"
   

2. Run the smoke test scripts.

   shellCopy

   cd paddleocr-text-recognition && uv run scripts/smoke_test.py && cd ..
   cd paddleocr-doc-parsing && uv run scripts/smoke_test.py && cd ..
   

   Use --skip-api-test to verify configuration only without making network requests. Use --test-url "https://..." to override the default sample document or image URL.