didit-liveness-detectionDetects liveness from a single selfie image via the Didit standalone API. Use when checking if a person is physically present, detecting spoofing or presenta...
Install via ClawdBot CLI:
clawdbot install rosasalberto/didit-liveness-detectionGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
POST → https://apx.didit.me/auth/v2/programmatic/register/`Calls external URL not in known-safe list
https://docs.didit.meAI Analysis
The skill's primary external API call (verification.didit.me) is consistent with its stated purpose of liveness detection. The flagged 'unknown' endpoints are for account registration and documentation, not part of the core image processing function, and do not indicate credential harvesting or data exfiltration from the user's interaction.
Audited Apr 16, 2026 · audit v1.0
Generated Mar 22, 2026
Banks and fintechs use this skill to verify customer identity during digital account opening, ensuring the selfie is from a live person to prevent fraud. It integrates into mobile apps to check liveness before KYC document submission, meeting regulatory anti-spoofing requirements.
Companies implement this skill for employee authentication to sensitive systems, replacing passwords with facial liveness checks. It detects presentation attacks like printed photos to prevent unauthorized access to internal networks or data.
Online platforms selling alcohol, tobacco, or adult content use this skill to verify user age via selfie liveness detection. It estimates age and gender while ensuring the image is from a real person, complying with legal age-gating laws.
Ride-sharing or delivery apps employ this skill to verify driver or courier identity periodically, preventing account sharing or spoofing. It checks liveness during shift start-ups to ensure the registered person is operating the service.
Telehealth services use this skill to confirm patient identity before virtual consultations, ensuring the person matches medical records. It helps prevent identity theft and meets HIPAA compliance by detecting deepfakes or screenshots.
Charge customers per liveness check API call, with tiered pricing based on volume (e.g., $0.10 per request for high-volume clients). This model suits startups or enterprises integrating the skill into existing apps without upfront costs.
Offer monthly or annual subscriptions that include a set number of liveness checks, with overage charges for additional usage. This provides predictable revenue and encourages long-term contracts from businesses like banks or SaaS platforms.
License the skill as a white-label product for other companies to rebrand and resell, targeting industries like identity verification providers. Revenue comes from licensing fees and a percentage of resale profits, scaling through partnerships.
💬 Integration Tip
Ensure the DIDIT_API_KEY is securely stored as an environment variable and test with sample images to handle edge cases like poor lighting or multiple faces.
Scored Jun 19, 2026
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