A facial analysis skill that estimates age and performs liveness detection to verify user identity and age compliance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install didit-age-estimation
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-age-estimation 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 Age Estimation?
The Didit Age Estimation skill provides a robust interface for estimating a person's age from a facial image using advanced deep learning models. It is designed for developers who need to implement age-gating, compliance checks, or facial analysis within their automation workflows. By utilizing this component of Openclaw Skills, users can achieve high accuracy with a Mean Absolute Error (MAE) as low as 1.5 years for individuals under 18.
Beyond simple estimation, the skill incorporates sophisticated liveness detection to mitigate spoofing attempts. It supports multiple security levels, including passive CNN-based checks for low-friction consumer applications and active 3D flash methods for high-security environments like banking and healthcare. This makes it an essential tool for maintaining regulatory standards while ensuring a smooth user experience.
Didit Age Estimation Use Cases
- Implementing age-gated access for restricted digital content or e-commerce products.
- Verifying user age during onboarding for financial services and healthcare apps.
- Enhancing identity security by combining age estimation with passive liveness detection.
- Automating compliance checks for regional age-of-majority laws across different countries.
How Didit Age Estimation Works
- The user provides a facial image in JPEG, PNG, WebP, or TIFF format through the Openclaw Skills interface.
- The skill transmits the image to the Didit standalone API using a secure x-api-key.
- The API processes the image to identify a single face and estimates the age based on facial features.
- A liveness check is performed simultaneously to ensure the image is a real person and not a spoofing attempt.
- The system returns a comprehensive JSON object containing the estimated age, liveness confidence score, and a status of Approved, Declined, or In Review.
Didit Age Estimation Setup
To get started with this skill, you need a valid API key from the Didit Business Console. Once obtained, configure your environment:
# Set the primary environment variable
export DIDIT_API_KEY="your_didit_api_key_here"
Ensure that the images processed are under 5MB and contain one clearly visible face for optimal performance within Openclaw Skills.
Didit Age Estimation Data Schema & Taxonomy
The skill organizes its output into a structured format for easy integration into larger workflows:
| Field | Type | Description |
|---|---|---|
status |
string | The result of the check: Approved, Declined, or In Review. |
age_estimation |
float | The estimated age in years (e.g., 24.3). |
score |
float | Liveness confidence score ranging from 0 to 100. |
method |
string | The liveness method used (PASSIVE, FLASHING, or ACTIVE_3D). |
warnings |
array | Diagnostic tags such as NO_FACE_DETECTED or AGE_BELOW_MINIMUM. |
Didit Age Estimation Advanced Features
- Adaptive workflow mode that triggers automatic ID verification fallback for borderline age cases.
- Per-country age restriction configurations to handle varying legal requirements globally.
- Multi-method liveness detection ranging from standard passive checks to high-security active 3D analysis.
- Automated image rotation logic to handle non-upright facial captures effectively.
- Seamless integration with the broader Openclaw Skills ecosystem for multi-agent identity verification.
SKILL.md
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