Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Estimates a person's age from a facial image using Didit's deep learning API. Returns an age estimate, liveness confidence score, and a pass/fail status in a single call. Supports configurable thresholds, per-country age restrictions, and an adaptive mode that falls back to ID verification for borderline cases.
Passive liveness detection runs in the same API call as age estimation, so spoofing checks require no separate verification step.
When to use it
- Age-gating access to alcohol or adult content websites
- Verifying buyer age at checkout for restricted products
- Enforcing minimum age on gambling or gaming platforms
- Blocking minors from signing up for age-restricted apps
- Per-country age restriction compliance with automatic ID fallback
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: add an age gate to my web app that blocks users under 18
- 1Capture a selfie from the user via webcam or file upload
- 2POST the image to the /v3/age-estimation/ endpoint with the x-api-key header set
- 3Read liveness.status from the response to get Approved, Declined, or In Review
- 4If Approved and age_estimation >= 18, grant access; if Declined, check warnings for AGE_BELOW_MINIMUM
- 5If In Review, trigger ID verification fallback or route to manual review
User either gains access or is blocked, with age_estimation and liveness score available for compliance logging
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.