Skill audit report
Detects liveness from a single selfie image via the Didit standalone API. Use when checking if a person is physically present, detecting spoofing or presentation attacks, implementing anti-spoofing measures, or performing passive liveness verification. Returns liveness score, face quality, and luminance metrics. 99.9% accuracy.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
26 GitHub stars
Stars/forks activity
WARN43
26 stars, 4 forks; issue activity unavailable in current metadata
Recent maintenance
PASS88
1mo since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN46
command execution surface, credential or environment access
Install availability
PASS92
npx skills add didit-protocol/skills --skill didit-liveness-detection
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL22
secrets or environment access, shell or command execution
Repository evidence
PASS86
https://github.com/didit-protocol/skills/tree/main/skills/didit-liveness-detection
Review status
INFO66
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add didit-protocol/skills --skill didit-liveness-detection
Repository
88
https://github.com/didit-protocol/skills/tree/main/skills/didit-liveness-detection
License
86
MIT
Maintenance
88
1mo since push
AI review
55
The script depends on the 'requests' library but does not include installation instructions in SKILL.md.
README/SKILL.md completeness
86
Usable description available
Dependency risk
46
command execution surface, credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
22
secrets or environment access, shell or command execution
Stars/forks activity
43
26 stars, 4 forks; issue activity unavailable in current metadata
Adoption
42
26 GitHub stars
Warnings
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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