Fish Aquatic Pet Health Diagnosis Analysis Tool | 鱼类水族宠物健康诊断分析工具

When a user provides a video URL or file of aquatic pets such as goldfish, koi, betta, shrimp, crab, etc. for analysis, this skill is triggered to perform aq...

This skill appears to implement the advertised remote aquarium-video analysis, but it also includes a large shared library (smyx_common), a local SQLite DAO, config files, and logic that reads workspace-level config and environment variables that the skill did not declare. Notably SKILL.md forbids reading 'local memory' but the code reads local config and can create/use a local database — that contradiction is important. Before installing or enabling this skill: 1) Ask the publisher to explain why a local DB and the face_analysis module are bundled and to confirm what local files will be read/written. 2) Inspect skills/smyx_common/scripts/config.yaml and config-dev/prod/test to verify API base URLs and any embedded keys. 3) Run the skill in an isolated test environment (or container) to observe what files it writes (attachments, DB). 4) Do not provide sensitive credentials or global environment variables (OPENCLAW_SENDER_OPEN_ID, FEISHU_OPEN_ID, workspace secrets) until you confirm their necessity. 5) If you expect a lightweight client that only forwards video to a trusted remote API, prefer a version without the local DAO and unrelated face-analysis components or require the author to minimize declared access and document exactly what is stored locally.

Type: OpenClaw Skill Name: smyx-aquarium-analysis Version: 1.0.0 The skill bundle is a specialized tool for aquatic pet health diagnosis that interfaces with a remote API (lifeemergence.com). It utilizes a shared library structure (smyx_common) to handle authentication, local token caching in a SQLite database (smyx-common-claw.db), and multi-step API polling. While the SKILL.md contains aggressive 'Mandatory Memory Rules' that instruct the AI to ignore local memory files in favor of cloud-synced data, this appears to be a design choice for data consistency rather than a malicious attempt to bypass security. The code includes some copy-paste artifacts (referencing 'autism_analysis' in SKILL.md), but no evidence of intentional data exfiltration or unauthorized system access was found.