Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add liortesta/ClawdAgent --skill face-recognitiongit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liortesta/clawdagent/face-recognition)<a href="https://agentmods.dev/skills/liortesta/clawdagent/face-recognition"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/face-recognition/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/liortesta/clawdagent/face-recognition"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/face-recognition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00790 |
| Opus 5 | $0.00000 | $0.00395 |
| Sonnet 5 | $0.00000 | $0.00158 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
face-recognition scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Face Recognition Security — FacePlugin
For authorized security testing and access control auditing only.
Overview
FacePlugin is a face recognition SDK used in security contexts for access control systems, identity verification auditing, and biometric security assessments. Understanding face recognition technology is critical for auditing biometric authentication systems.
Core Capabilities
- Face Detection: Locate faces in images/video with bounding boxes
- Face Recognition: 1:1 verification (is this person who they claim?) and 1:N identification (who is this person?)
- Liveness Detection: Anti-spoofing checks (photo attack, video replay, 3D mask detection)
- Age/Gender Estimation: Demographic analysis for access control policies
- Face Quality Assessment: Image quality checks for enrollment suitability
- Multi-Face Processing: Detect and process multiple faces in a single frame
Security Auditing Applications
Access Control System Audit
Biometric System → Security Assessment
├── Liveness Detection Testing
│ ├── Photo attack (printed photo)
│ ├── Video replay attack (recorded video)
│ ├── 3D mask attack
│ └── Deepfake detection capability
├── False Accept Rate (FAR) Testing
│ ├── Similar-looking individuals
│ ├── Twins/relatives testing
│ └── Cross-demographic testing
├── False Reject Rate (FRR) Testing
│ ├── Lighting variations
│ ├── Aging effects
│ ├── Accessories (glasses, masks, hats)
│ └── Expression variations
└── System Resilience
├── Database security (template storage)
├── API security (authentication endpoints)
├── Network interception (template in transit)
└── Enrollment process integrity
Key Security Concerns in Face Recognition Systems
- Presentation Attacks: Spoofing with photos, videos, masks, or deepfakes
- Template Theft: Extracting stored face templates from database
- Adversarial Attacks: Subtle perturbations that fool the system
- Bias Testing: Ensure equal accuracy across demographics (skin tone, age, gender)
- Privacy Compliance: GDPR/CCPA compliance for biometric data storage
- API Security: Authentication and rate limiting on face recognition endpoints
- Data Minimization: Ensure only necessary biometric data is stored
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 81 lines · 0 tokens per session scan A 480328f64a26
face-recognition is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 14d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 790 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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