didit-age-estimation
Integrate Didit Age Estimation standalone API to estimate a person's age from a facial image. Use when the user wants to estimate age, verify age, implement age gating, check if someone is over 18/21, perform age verification for compliance, or use facial analysis for age detection using Didit. Includes passive liveness check. Supports configurable thresholds, adaptive age estimation with ID verification fallback, and per-country age restrictions.
Why use this skill?
Integrate Didit Age Estimation into OpenClaw for accurate facial age detection and passive liveness checks. Ideal for age gating and compliance.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/rosasalberto/didit-age-estimationWhat This Skill Does
The didit-age-estimation skill enables OpenClaw to perform accurate, automated age estimation and passive liveness detection using the Didit standalone API. By processing a user's facial image, this skill provides a reliable age verification mechanism suitable for compliance, age gating, and identity verification. The service employs sophisticated deep learning models to estimate age with high precision (MAE ±3.5 years generally; ±1.5 years for minors) and includes a liveness check to prevent spoofing attempts, such as the use of photos or pre-recorded videos.
Installation
You can install this skill directly via the OpenClaw command-line interface using the following command:
clawhub install openclaw/skills/skills/rosasalberto/didit-age-estimation
Ensure that you have your API key ready from the Didit Business Console, as it will be required to authenticate your requests.
Use Cases
This skill is ideal for businesses and applications that require age-sensitive access control. Common use cases include:
- Age Gating: Automatically restricting access to content or products (e.g., alcohol, tobacco, adult content) based on configurable age thresholds.
- Regulatory Compliance: Implementing age verification processes to meet legal requirements regarding minors in digital services.
- Financial Services: Performing initial identity and age checks during customer onboarding or high-friction transactions.
- Consumer Apps: Providing a low-friction, passive way to confirm a user's age without requiring manual document uploads in early-stage verification.
Example Prompts
- "Check if the user in this photo is over 18 years old using the Didit estimation tool."
- "Perform an age estimation on the provided image and confirm the liveness score is above 80 before granting access."
- "Verify if the customer is an adult; if they appear to be under 21, trigger the fallback ID verification workflow."
Tips & Limitations
- Quality Matters: The API requires one clearly visible face per image. Blurry, poorly lit, or obstructed faces will negatively impact accuracy.
- File Specifications: Ensure images are under 5MB and adhere to JPEG, PNG, WebP, or TIFF formats. Using the
rotate_imageparameter is recommended if your input source often provides non-upright orientation. - Strategic Usage: While the passive liveness check is convenient, consider that for high-risk financial or banking transactions, you should explore the Didit API's more advanced ACTIVE_3D or FLASHING methods if supported, as these offer even higher security than standard passive CNN detection.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-rosasalberto-didit-age-estimation": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-read, data-collection, external-api
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