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Install in OpenClaw
/install sam-faces
Description
Face recognition and identity memory for AI assistants. Enroll known people with reference photos, then automatically identify faces in inbound images — with...
Usage Guidance
Before installing or enabling this skill, consider the following:
- Review the sam-faces PyPI project and source code before pip installing (pip packages can run arbitrary code at install and runtime).
- Understand where face data will be stored on disk (the SKILL.md references {workspaceDir}/faces/) and ensure that location is acceptable and secured; the skill does not declare or request these paths explicitly.
- The skill's inference is local, but it instructs the agent to prepend identified names into LLM prompts (llm_context). If your assistant uses remote LLM APIs, that will transmit personally identifiable information to third parties — disable automatic injection or require explicit user consent per image.
- If you need stricter privacy, require explicit user confirmation before identification or enrollment and audit what is being sent to models or external services.
- If you proceed, install only on trusted devices, limit filesystem access to a dedicated workspace, and consider legal/privacy implications of storing and processing biometric identifiers in your jurisdiction.
Capability Analysis
Type: OpenClaw Skill
Name: sam-faces
Version: 1.1.0
The sam-faces skill provides local face recognition and identity management using a PyPI package. The SKILL.md file contains standard instructions for the AI agent to automatically identify faces in images and manage a local database (people.db) without external API calls. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found.
Capability Assessment
Purpose & Capability
Name and description match the required binary (sam-faces) and CLI invocations. Asking for a sam-faces binary is coherent for face recognition. However, the skill references a workspaceDir and persistent databases for encodings without declaring any required config/path or permissions; that is an unstated requirement.
Instruction Scope
Runtime instructions tell the agent to automatically identify faces when users send images and to prepend the produced llm_context (names, confidences, positions) into image descriptions used for LLM prompts. That creates a clear privacy/exfiltration vector: even though inference is local, identity data would be injected into LLM prompts which may be sent to remote model APIs. Instructions also assume the agent can write temp files and to {workspaceDir}/faces/ — the skill does not declare or request explicit file-paths or permissions.
Install Mechanism
The registry lists no install spec, but SKILL.md includes a pip install suggestion (pip package 'sam-faces'). Installing from PyPI is plausible for this tool, but pip installs run arbitrary package install scripts. The absence of an explicit registry-level install spec plus the instruction-only nature means installers may not automatically vet or run that install; users should review the PyPI package source before installing.
Credentials
No environment variables or credentials are requested (which is appropriate). However, the skill stores face encodings and unknown face crops on-disk ({workspaceDir}/faces/people.db and /faces/unknown/) yet doesn't declare or require a workspace path or explicit file permissions. The combination of stored identities plus the instruction to include identities in LLM context is disproportionate without explicit safeguards or consent controls.
Persistence & Privilege
always:false (no forced always-on presence) and autonomous invocation is allowed by default. Autonomous invocation plus automatic face ID behavior is potentially risky: the agent could insert PII into prompts without additional per-message consent. This is not a configuration bug by itself, but it amplifies privacy risk and should be managed via user-facing controls (e.g., opt-in, per-image consent).
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install sam-faces - After installation, invoke the skill by name or use
/sam-faces - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
Add visualize command for drawing bounding boxes and name labels on detected faces
v1.0.5
Add visualize command to documentation (README + SKILL.md)
v1.0.4
Remove accidentally restored setup.sh script, keep only pip install
v1.0.3
Add threshold tuning reference guide and setup script
v1.0.2
Fix JSON output example in docs to match actual CLI output
v1.0.1
Fix privacy framing and remove stale setup.sh script
v1.0.0
Initial ClawHub release — face recognition and identity memory for OpenClaw agents
Metadata
Frequently Asked Questions
What is sam-faces?
Face recognition and identity memory for AI assistants. Enroll known people with reference photos, then automatically identify faces in inbound images — with... It is an AI Agent Skill for Claude Code / OpenClaw, with 105 downloads so far.
How do I install sam-faces?
Run "/install sam-faces" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is sam-faces free?
Yes, sam-faces is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does sam-faces support?
sam-faces is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created sam-faces?
It is built and maintained by Sam Cox (@jasonacox-sam); the current version is v1.1.0.
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