Pet Detection Skill | 宠物检测技能

Detects cats, dogs, and birds appearing in the target area; supports video stream and image detection, suitable for home pet monitoring scenarios. | 宠物检测技能，检...

Key points to consider before installing or running this skill: - Data exfiltration: The skill uploads user-provided media (local files or URLs) to remote API endpoints defined in the included config; if the media contain sensitive information (e.g., audio/video from inside your home), do not run it until you trust the remote host. The repo's default config references lifeemergence.com production/test URLs — verify and confirm the destination. - Inconsistent guarantees: SKILL.md explicitly forbids reading local memory, but the code will create/read YAML config files and a local SQLite DB in the workspace/data directory; these persistent writes break the stated prohibition. Expect local files to be created. - Credentials and open-id: The skill requires an open-id (CLI arg or read from config); it can also accept an API key. The registry metadata does not declare these env/credential needs — be cautious about providing any tokens or IDs. Prefer supplying a throwaway test open-id and avoid providing other credentials. - Large dependency surface: The package includes a big common library and a long requirements list. If you plan to install dependencies, review them and install in an isolated environment (container/VM) to limit exposure. - Recommended mitigations: run first in a sandboxed environment, inspect network traffic (or run with outbound network disabled) to confirm endpoints, inspect and sanitize config YAML files, and consider running against non-sensitive test media. If you require a privacy-preserving local solution, do not use this skill until you can confirm the API provider and hosting are trusted.

Type: OpenClaw Skill Name: smyx-pet-detection-analysis Version: 1.0.0 The skill bundle provides pet detection and health analysis by interfacing with a remote cloud API (lifeemergence.com). It utilizes a shared utility library (smyx_common) to manage authentication tokens stored in a local SQLite database and handles media uploads via multipart/form-data. While SKILL.md contains 'Mandatory Memory Rules' that instruct the AI agent to bypass local memory files in favor of cloud-fetched data, these appear to be functional constraints to ensure data consistency rather than malicious prompt injection. The scripts (pet_detection_analysis.py, api_service.py) follow standard API integration patterns and do not exhibit signs of data theft or unauthorized command execution.