A professional-grade identity verification skill for automating OCR, MRZ parsing, and authenticity checks across 4000+ global document types.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install didit-id-document-verification
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install didit-id-document-verification using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Didit ID Document Verification?
The Didit ID Document Verification skill enables AI agents to verify identity documents via the Didit standalone API. It supports a massive range of documents, including passports, national IDs, driver's licenses, and residence permits across 220+ countries and 130 languages. By integrating this into Openclaw Skills, developers can automate the complex pipeline of OCR extraction, MRZ parsing, and document liveness detection.
This skill is designed for high-security environments, offering intelligent capture detection, template matching, and tamper detection. It ensures that the documents provided are original, real-time photos rather than screenshots or digital copies, providing a robust layer of security for any application requiring identity validation.
Didit ID Document Verification Use Cases
- Automating KYC (Know Your Customer) procedures for fintech or e-commerce platforms.
- Extracting structured personal data from identity documents for automated form filling.
- Verifying the authenticity of government-issued IDs to prevent fraudulent account creation.
- Integrating document-based identity checks into multi-step identity verification pipelines.
How Didit ID Document Verification Works
- The agent captures or receives high-quality images of the front and back of an identity document.
- The skill transmits these images to the Didit API using a secure API key.
- The processing pipeline performs intelligent capture detection and identifies the specific document type.
- OCR text extraction and MRZ/barcode parsing are executed to gather personal details.
- Authenticity checks, including template matching and tamper detection, are performed against global standards.
- A comprehensive JSON response is returned, detailing the approval status, extracted data, and any security warnings.
Didit ID Document Verification Setup
To get started with this skill in Openclaw Skills, you must configure your environment with a valid API key.
# Set your Didit API key
export DIDIT_API_KEY="your_api_key_here"
If you do not have an account, you can register and verify your email via the Didit programmatic API to receive your credentials. Once configured, you can use utility scripts to verify documents directly from the CLI:
python scripts/verify_id.py front_image.jpg back_image.jpg
Didit ID Document Verification Data Schema & Taxonomy
The skill returns a structured dataset representing the verified identity. Below are the primary fields included in the response:
| Field | Type | Description |
|---|---|---|
status |
string | The verification result: Approved, Declined, or In Review. |
document_type |
string | The category of the document (e.g., Passport, Identity Card). |
document_number |
string | The unique identifier found on the document. |
mrz |
object | Machine Readable Zone data including surname, given name, and expiry. |
parsed_address |
object | Geocoded address components including city, postal code, and country. |
warnings |
array | Risk-related tags such as SCREEN_CAPTURE_DETECTED or DOCUMENT_EXPIRED. |
Didit ID Document Verification Advanced Features
- Coverage for over 4,000 document types across 220+ countries and 130+ languages.
- Advanced liveness detection to identify screen captures, printed copies, and portrait manipulation.
- Configurable workflow triggers based on specific warning tags like MRZ_VALIDATION_FAILED or DOCUMENT_COUNTRY_MISMATCH.
- Seamless integration with the broader Openclaw Skills ecosystem for face-matching and AML screening pipelines.
- Automated geocoding and address formatting for extracted residency data.
SKILL.md
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