← 返回 Skills 市场
18072937735

Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具

作者 smyx-skills · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
60
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install smyx-respiratory-symptom-recognition-analysis
功能描述
Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly ale...
安全使用建议
Key points before installing or using this skill: - Privacy: The skill uploads video files (potentially showing people's faces and breathing) to remote AI APIs. Only upload videos you are allowed to share and avoid sensitive personal data unless you trust the remote service and its data handling policies. - Undeclared config/credentials: The manifest lists no required env vars, but the code will read environment variables (OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, FEISHU_OPEN_ID) and module config.yaml files that may contain API URLs and api-key values. Check all config files (skills/smyx_common/scripts/config.yaml and skills/{this_skill}/skills/smyx_common/scripts/config.yaml) for endpoints or keys before running. - Local persistence: The skill will save uploaded attachments to its attachments directory and the shared smyx_common DAO will create a SQLite DB under the workspace data directory. If you run it in a shared workspace, expect persistent files. If you want containment, run the skill in an isolated workspace or container. - Incoherent rules: SKILL.md forbids reading local memory, but the code contains local storage and configuration loaders. Ask the author to clarify the intended behavior (where history is saved, and what the prohibition on local memory means in practice). - Dependencies: There are large requirements files but no install instructions. Decide how you will manage dependencies (virtualenv/container) and avoid auto-running untrusted install scripts. - API endpoints: The provided config files reference domains like lifeemergence.com and example dev/local IPs. Verify the production API URL before sending data; consider auditing the remote API provider's privacy/security practices. If you are not comfortable with any of the above, do not upload real patient or sensitive videos and run the skill in an isolated environment. If possible, request from the skill author explicit documentation of runtime network endpoints, what data is transmitted, retention policies, and confirm the intended handling of local memory vs. cloud history.
功能分析
Type: OpenClaw Skill Name: smyx-respiratory-symptom-recognition-analysis Version: 1.0.0 The skill bundle implements a health monitoring tool that collects sensitive user identifiers (phone numbers/usernames) and uploads video data to a remote backend (lifeemergence.com). It contains 'highest priority' instructions in SKILL.md that explicitly command the AI agent to bypass its standard local memory and LanceDB retrieval systems in favor of the developer's cloud API, which constitutes aggressive prompt steering. Furthermore, the common utility scripts (smyx_common/scripts/skill.py) include a high-risk capability to recursively execute the 'openclaw' CLI via subprocess.run, and the bundle maintains a local SQLite database (smyx-common-claw.db) to persist session tokens and user metadata.
能力标签
requires-sensitive-credentials
能力评估
Purpose & Capability
The name/description (video-based respiratory symptom recognition) aligns with the provided scripts: analysis entrypoints, face/respiratory modules, and API client code all implement video upload and remote analysis. However, the skill declares no required environment variables or credentials while its code expects/reads configuration files, environment variables (e.g. OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, FEISHU_OPEN_ID) and can use API keys present in config files. That mismatch (no declared env/credentials but runtime reliance on them) is surprising and reduces transparency.
Instruction Scope
SKILL.md contains explicit runtime rules (forbid reading local memory, strict open-id retrieval flow, auto-save attachments) but the codebase also includes a local SQLite DAO, utilities to read module config.yaml files, and logic that will read environment variables (CURRENT__OPEN_ID) and module config files under skills/smyx_common. The instruction to never read local memory contrasts with the presence of local storage logic; the skill will upload user videos to remote API endpoints (expected for remote analysis) but that is sensitive and must be considered. SKILL.md forbids falling back to local memory for history, yet the code contains local persistence utilities—this incoherence should be clarified.
Install Mechanism
There is no install specification (lowest-risk delivery), but the repository contains multiple requirements.txt files and a large common dependency list (skills/smyx_common/requirements.txt) implying substantial Python packages will be needed at runtime. Without an install spec, users won't know whether dependencies will be installed automatically or are expected to exist. The dependency footprint is large relative to a single recognition script and deserves attention.
Credentials
The skill declares no required env vars or primary credential, but code reads and uses several environment/config sources: OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, OPENCLAW_SENDER_USERNAME, FEISHU_OPEN_ID, and ApiEnum/ConstantEnum values loaded from module config.yaml files (which may include API URLs and api-key fields). The SKILL.md enforces an open-id retrieval flow (from two config.yaml locations or explicit user input) and forbids generating defaults, but the code will accept open-id via environment and will also use API endpoints and api-keys from config files if present. The lack of declared credentials is disproportionate to the actual runtime requirements and reduces transparency about what secrets might be used.
Persistence & Privilege
always:false (no forced inclusion). The skill writes uploaded attachments to a local attachments directory and the common library includes a DAO that creates a local SQLite database under a workspace data directory. That gives the skill local persistent storage (files and DB) scoped to its workspace; this is not necessarily malicious but contrasts with SKILL.md's ban on reading local memory and should be understood by the user. The skill does not request elevated platform privileges or modify other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install smyx-respiratory-symptom-recognition-analysis
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /smyx-respiratory-symptom-recognition-analysis 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of Respiratory Symptom Recognition Analysis: - Introduces automatic detection and frequency analysis of coughing, expectoration (phlegm), and wheezing from video using computer vision. - Generates structured health reports, trend charts, severity assessments, and anomaly alerts based on real-time video analysis. - Enforces strict rules to obtain user open-id before processing; forbids use of any local or long-term memory for history retrieval—history must always be fetched from the cloud API. - Supports default and keyword-triggered operation modes for both live video analysis and historical report queries. - Outputs historical analysis reports in Markdown tables with direct links for user convenience.
元数据
Slug smyx-respiratory-symptom-recognition-analysis
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 是什么?

Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly ale... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 60 次。

如何安装 Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install smyx-respiratory-symptom-recognition-analysis」即可一键安装,无需额外配置。

Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 是免费的吗?

是的,Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 支持哪些平台?

Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具?

由 smyx-skills(@18072937735)开发并维护,当前版本 v1.0.0。

💬 留言讨论