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...

Security Scan

The skill mostly does what it says (calls a remote aquarium-health API for video analysis) but includes inconsistent and disproportionate behaviors — local DB/DAO code, undeclared environment access, and contradictions between the SKILL.md rules and the code — so review before installing.

Purpose & Capability

The stated purpose (analyze aquarium pet videos via a server-side API) matches many of the scripts (scripts/aquarium_analysis.py, scripts/api_service.py). However the repo also bundles a separate face_analysis skill and a large 'smyx_common' library (DB/DAO, many utilities). Inclusion of a full local DAO/SQLite layer and a broad dependency list is heavier than expected for a thin client that just calls a remote API. Having a local DB + many unrelated analysis modules is plausible (reused common code), but is more privileged and wider in scope than the simple description implies.

Instruction Scope

SKILL.md imposes strict runtime rules (e.g., 'absolute prohibition' on reading local memory/LanceDB and 'all history must come from cloud API') but the codebase includes modules that read local config files and implement a local SQLite DAO which writes/reads under the workspace data directory. The SKILL.md mandates saving uploaded attachments to an attachments folder; that implies writing files. The runtime instructions also require checking local files for open-id in specific config paths. These behaviors contradict the 'do not read local memory' rule and grant the skill file-system access beyond what is explicitly justified.

Install Mechanism

There is no install spec (instruction-only), so nothing is auto-downloaded on install. However the included smyx_common/requirements.txt lists many packages and the repo contains many Python modules that assume those dependencies exist. Installing or running this skill in a real agent may require installing a large dependency set (moderate friction and risk). No external arbitrary download URLs or extract operations were found.

Credentials

The skill declares no required env vars, but the code reads multiple environment values (OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, OPENCLAW_SENDER_USERNAME, FEISHU_OPEN_ID) and also expects to read api-key/open-id from local config.yaml files. SKILL.md enforces a multi-step open-id discovery that reads local config paths in the skill and workspace; this grants the skill access to workspace-level config. The skill therefore implicitly needs environment/config access that is not declared, which is disproportionate and inconsistent.

Persistence & Privilege

The code includes a sqlite-based DAO that creates/uses a database under the workspace data directory and utility functions that create config yaml files if missing. The SKILL.md instructs saving uploaded attachments into the skill directory. Although 'always' is false, the skill will persist data locally and can read workspace config files and possibly other skill config paths — a broader persistence/privilege than the simple description implies.

What to consider before installing

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.

Designed specifically for aquarium enthusiasts, this intelligent health monitoring assistant aims to solve the pain points of "difficult diagnosis and late detection" for underwater pets. When users upload video files or network URLs featuring goldfish, koi, betta fish, shrimp, or crabs, the system immediately triggers a deep analysis protocol.
By leveraging advanced server-side APIs, the tool performs frame-by-frame parsing of the footage to precisely capture key physiological traits, including scale integrity, fin extension, body color luster, and swimming activity. Whether it's Ich (white spot disease), fin rot, dull coloration, or abnormal lethargy, the system敏锐ly identifies these signs. Combining this with water quality factors for a comprehensive assessment, it ultimately generates a detailed "Pet Safety Guardian Health Report," empowering users to intervene early and safeguard the vitality of their beloved aquatic companions.

本工具是一款专为水族爱好者设计的智能化健康监测助手，旨在解决水下宠物“看病难、发现晚”的痛点。当用户上传金鱼、锦鲤、斗鱼、虾、蟹等水族宠物的视频文件或网络视频URL时，系统将立即触发深度分析程序。通过调用先进的服务端API，工具能够对视频画面进行逐帧解析，精准捕捉宠物的鳞片完整性、鱼鳍舒展度、体色光泽度以及游动活跃度等关键生理特征。无论是白点病、烂鳍、体色暗淡还是异常呆滞，系统均能敏锐识别，并结合水质环境因素进行综合研判，最终生成一份详尽的“宠安卫士健康报告”，帮助用户在疾病早期及时干预，守护爱宠的生命活力。

演示案例

• 🔗 通过网路视频进行识别分析
• 🔗 通过上传视频进行识别分析
• 🔗 显示历史分析报告

⚠️ 强制记忆规则（最高优先级）

本技能明确约定：

• 绝对禁止读取任何本地记忆文件：包括但不限于 memory/YYYY-MM-DD.md、MEMORY.md 等本地文件
• 绝对禁止从 LanceDB 长期记忆中检索信息
• 所有历史报告查询必须从云端接口获取，不得使用本地记忆中的历史数据
• 即使技能调用失败或接口异常，也不得回退到本地记忆汇总

任务目标

• 本 Skill 用于：通过水族宠物视频进行鱼类宠物健康诊断分析，获取结构化的宠安卫士健康报告
• 能力包含：视频分析、鳞片完整性识别、鱼鳍状况评估、体色变化分析、活跃度检测、常见鱼病预警、水质适应性养护建议生成
• 触发条件:
  1. 默认触发：当用户提供鱼类宠物/水族宠物视频 URL 或文件需要分析时，默认触发本技能进行鱼类宠物健康诊断分析
  2. 当用户明确需要进行鱼类健康检查时，提及鱼类宠物、金鱼、锦鲤、斗鱼、虾、蟹、水族、鱼宠健康、鱼宠诊断等关键词，并且上传了视频文件或者图片文件
  3. 当用户提及以下关键词时，自动触发历史报告查询功能 ：查看历史鱼宠报告、历史宠安报告、鱼宠诊断报告清单、鱼宠报告清单、查询历史报告、查看鱼宠报告列表、显示所有鱼宠报告、显示鱼宠诊断报告，查询宠安卫士健康报告
• 自动行为：
  1. 如果用户上传了附件或者视频/图片文件，则自动保存到技能目录下 attachments
  2. ⚠️ 强制数据获取规则（次高优先级）：如果用户触发任何历史报告查询关键词（如"查看所有鱼宠报告"、"显示所有宠安报告"、" 查看历史报告"等），必须：
     • 直接使用 python -m scripts.autism_analysis --list --open-id 参数调用 API 查询云端的历史报告数据
     • 严格禁止：从本地 memory 目录读取历史会话信息、严格禁止手动汇总本地记录中的报告、严格禁止从长期记忆中提取报告
     • 必须统一从云端接口获取最新完整数据，然后以 Markdown 表格格式输出结果

前置准备

• 依赖说明:scripts 脚本所需的依赖包及版本

  requests>=2.28.0
  

操作步骤

🔒 open-id 获取流程控制（强制执行，防止遗漏）

在执行鱼类宠物健康分析前，必须按以下优先级顺序获取 open-id：

第 1 步：【最高优先级】检查技能所在目录的配置文件（优先）
        路径：skills/smyx_common/scripts/config.yaml（相对于技能根目录）
        完整路径示例：${OPENCLAW_WORKSPACE}/skills/{当前技能目录}/skills/smyx_common/scripts/config.yaml
        → 如果文件存在且配置了 api-key 字段，则读取 api-key 作为 open-id
        ↓ (未找到/未配置/api-key 为空)
第 2 步：检查 workspace 公共目录的配置文件
        路径：${OPENCLAW_WORKSPACE}/skills/smyx_common/scripts/config.yaml
        → 如果文件存在且配置了 api-key 字段，则读取 api-key 作为 open-id
        ↓ (未找到/未配置)
第 3 步：检查用户是否在消息中明确提供了 open-id
        ↓ (未提供)
第 4 步：❗ 必须暂停执行，明确提示用户提供用户名或手机号作为 open-id

⚠️ 关键约束：

• 禁止自行假设,自行推导,自行生成 open-id 值（如 openclaw-control-ui、default、fishC113、fish123 等）
• 禁止跳过 open-id 验证直接调用 API
• 必须在获取到有效 open-id 后才能继续执行分析
• 如果用户拒绝提供 open-id，说明用途（用于保存和查询鱼宠报告记录），并询问是否继续

---

• 标准流程:
  1. 准备视频输入
     • 提供本地视频文件路径或网络视频 URL
     • 确保视频清晰展示鱼儿整体外观、鳞片、鱼鳍、游动姿态，光线充足
  2. 获取 open-id（强制执行）
     • 按上述流程控制获取 open-id
     • 如无法获取，必须提示用户提供用户名或手机号
  3. 执行鱼类宠物健康分析
     • 调用 -m scripts.aquarium_analysis 处理视频文件（必须在技能根目录下运行脚本）
     • 参数说明:
       • --input: 本地视频文件路径（使用 multipart/form-data 方式上传）
       • --url: 网络视频 URL 地址（API 服务自动下载）
       • --fish-type: 鱼类宠物类型，可选值：goldfish/koi/betta/shrimp/crab/turtle/clownfish/guppy/arowana/angel/other，默认 other
       • --open-id: 当前用户的 open-id（必填，按上述流程获取）
       • --list: 显示鱼类宠物视频历史分析报告列表清单（可以输入起始日期参数过滤数据范围）
       • --api-key: API 访问密钥（可选）
       • --api-url: API 服务地址（可选，使用默认值）
       • --detail: 输出详细程度（basic/standard/json，默认 json）
       • --output: 结果输出文件路径（可选）
  4. 查看分析结果
     • 接收结构化的宠安卫士健康报告
     • 包含：鱼类宠物基本信息、整体健康状况、鳞片分析、鱼鳍状态、体色分析、潜在疾病预警、健康养护建议

资源索引

• 必要脚本：见 scripts/aquarium_analysis.py(用途：调用 API 进行水族宠物健康分析，本地文件使用 multipart/form-data 方式上传，网络 URL 由 API 服务自动下载)
• 配置文件：见 scripts/config.py(用途：配置 API 地址、默认参数和视频格式限制)
• 领域参考：见 references/api_doc.md(何时读取：需要了解 API 接口详细规范和错误码时)

注意事项

• 仅在需要时读取参考文档，保持上下文简洁
• 视频要求：支持 mp4/avi/mov 格式，最大 100MB
• API 密钥可选，如果通过参数传入则必须确保调用鉴权成功，否则忽略鉴权
• 分析结果仅供健康参考，不能替代专业宠医诊断
• 禁止临时生成脚本，只能用技能本身的脚本
• 传入的网路地址参数，不需要下载本地，默认地址都是公网地址，api 服务会自动下载
• 当显示历史分析报告清单的时候，从数据 json 中提取字段 reportImageUrl 作为超链接地址，使用 Markdown 表格格式输出，包含" 报告名称"、"鱼宠类型"、"分析时间"、"点击查看"四列，其中"报告名称"列使用鱼宠健康分析报告-{记录id}形式拼接, "点击查看"列使用 [🔗 查看报告](reportImageUrl) 格式的超链接，用户点击即可直接跳转到对应的完整报告页面。
• 表格输出示例：

  报告名称	鱼宠类型	分析时间	点击查看
  鱼宠健康分析报告 -20260312172200001	金鱼	2026-03-12 17:22:00	🔗 查看报告

使用示例

# 分析本地金鱼视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --input /path/to/goldfish_video.mp4 --fish-type goldfish --open-id openclaw-control-ui

# 分析网络锦鲤视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --url https://example.com/koi_video.mp4 --fish-type koi --open-id openclaw-control-ui

# 分析本地斗鱼视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --input /path/to/betta_video.mp4 --fish-type betta --open-id openclaw-control-ui

# 分析本地观赏虾视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --input /path/to/shrimp_video.mp4 --fish-type shrimp --open-id openclaw-control-ui

# 分析本地螃蟹视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --input /path/to/crab_video.mp4 --fish-type crab --open-id openclaw-control-ui

# 分析本地乌龟视频（以下只是示例，禁止直接使用openclaw-control-ui 作为 open-id）
python -m scripts.aquarium_analysis --input /path/to/turtle_video.mp4 --fish-type turtle --open-id openclaw-control-ui

# 显示历史分析报告/显示分析报告清单列表/显示历史宠安报告（自动触发关键词：查看历史鱼宠报告、历史报告、鱼宠报告清单等）
python -m scripts.aquarium_analysis --list --open-id openclaw-control-ui

# 输出精简报告
python -m scripts.aquarium_analysis --input video.mp4 --fish-type goldfish --open-id your-open-id --detail basic

# 保存结果到文件
python -m scripts.aquarium_analysis --input video.mp4 --fish-type koi --open-id your-open-id --output result.json