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Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能
作者
smyx-skills
· GitHub ↗
· v1.0.0
· MIT-0
77
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0
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0
当前安装
1
版本数
在 OpenClaw 中安装
/install smyx-elderly-bed-exit-wandering-monitoring-analysis
功能描述
Identifies abnormal behaviors such as getting out of bed at night, prolonged wandering, and remaining motionless for extended periods. It is suitable for nig...
安全使用建议
Key points before you install/use this skill:
- Data flow & privacy: The skill uploads video files to remote API endpoints (API base URLs appear in skills/smyx_common config YAML and references point to lifeemergence domains). If you install/use it, expect personal/night-video footage to be transmitted to those servers. Verify and trust the service operator before sending sensitive footage.
- Credentials & config: SKILL.md forces an open-id and reads config files under skills/smyx_common/scripts/config.yaml or workspace-level config. Inspect those config files and any API keys before use. The code also reads environment vars (OPENCLAW_SENDER_OPEN_ID etc.) even though the skill declares no required env—this is inconsistent.
- Local persistence: The package contains a local DAO that creates a SQLite DB under the workspace (workspace/data/*.db) and may create an attachments directory. SKILL.md forbids using local 'memory' for historical queries but the codebase still contains local storage mechanisms—decide whether you accept local persistence of metadata.
- Autonomy and triggering: The skill can be invoked automatically by keywords (per SKILL.md). If you enable autonomous invocation, it could upload saved attachments or call the API when those keywords appear. Only allow autonomous invocation if you trust the code and remote service.
- Unknown provenance: There is no homepage and the source is listed as unknown. That increases risk—prefer skills from known, audited authors.
- Practical steps: (1) Review the RequestUtil implementation (skills/smyx_common/scripts/util.py) to see exact HTTP destinations, headers, and what metadata is transmitted. (2) Confirm the API base URL(s) in skills/smyx_common/scripts/config.yaml (and any env overrides) and decide whether you trust those domains. (3) Test using non-sensitive video data first. (4) If you need to avoid remote transmission, do not run the scripts as-is; consider isolating them or modifying to run a local-only model.
- What would change this assessment: seeing RequestUtil code that proves uploads are strictly local-only (no external endpoints), documented, trusted server endpoints, or a trustworthy upstream source/homepage would raise confidence toward benign. Conversely, discovering hard-coded unknown external endpoints or hidden upload behavior would increase the risk rating.
功能分析
Type: OpenClaw Skill
Name: smyx-elderly-bed-exit-wandering-monitoring-analysis
Version: 1.0.0
The skill bundle implements an elderly monitoring system that interfaces with a cloud-based API (lifeemergence.com) for behavioral analysis. It uses a local SQLite database (smyx-common-claw.db) to cache session tokens and user identifiers, and the RequestUtil class manages authentication via a phone-based login endpoint. While the SKILL.md contains highly prescriptive 'Mandatory Memory Rules' that instruct the AI agent to ignore local memory files in favor of cloud data, these appear to be operational constraints designed to ensure data consistency rather than malicious prompt injections. The code logic is consistent with the stated purpose of providing safety monitoring and historical report retrieval.
能力标签
能力评估
Purpose & Capability
The name/description align with the included code: scripts call remote AI analysis endpoints and provide video upload/Report-list functionality. The package includes related subskills (face_analysis) and a common library (smyx_common) that implement networked API calls and result formatting, which is coherent with a cloud-based analysis service. Note: the skill bundles a local DAO/SQLite layer and many utility modules even though the SKILL.md emphasizes always using the cloud for history queries—this is unexpected but could be intended for caching or other local bookkeeping.
Instruction Scope
SKILL.md enforces strict runtime rules (forbid reading local memory/LanceDB, mandate always fetching history from cloud, require explicit open-id resolution steps, save uploaded attachments to attachments directory). The actual code: (a) implements API-driven upload/listing (expected); (b) uses a local DAO and filesystem paths (workspace/data SQLite) which suggests local persistence even though SKILL.md forbids local-memory fallbacks for history; (c) SKILL.md claims attachments will be auto-saved but the visible scripts primarily read local files and upload them to remote API – the save-to-attachments behavior isn't obviously implemented in the shown code. The forced open-id resolution flow (read config files under skills/smyx_common or workspace) is unusual and could cause the skill to read config files for credentials or identifiers.
Install Mechanism
There is no install spec (instruction-only + packaged scripts) so nothing is automatically downloaded from third-party URLs at install time. That lowers installer risk. However the repo includes large requirements.txt files (smyx_common, face_analysis) listing many dependencies; although not auto-installed, a user who follows 'pip install -r' would pull many packages. No external binary downloads or URL-extract installs were declared.
Credentials
The skill declares no required environment variables, but the code reads environment values (OPENCLAW_SENDER_OPEN_ID, OPENCLAW_SENDER_USERNAME, FEISHU_OPEN_ID) and relies on config YAMLs (skills/smyx_common/scripts/config.yaml or workspace config) for API base URLs and api-key. The SKILL.md also requires obtaining an open-id (from config files or user) before operation. The mismatch between 'no env required' and actual env/config usage is an incoherence. Also the skill will upload potentially sensitive videos and metadata to remote endpoints (configured in the YAMLs).
Persistence & Privilege
The included smyx_common.dao writes/reads a local SQLite DB under the workspace data directory and the code path-building will create directories and files. SKILL.md forbids reading local memory for historical queries, but the codebase clearly supports local persistence (DB), which is a mismatch. The skill does not declare always:true and does not modify other skills' configs, but it will create local files under the workspace (attachments, data DB), so it has persistent local footprint.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install smyx-elderly-bed-exit-wandering-monitoring-analysis - 安装完成后,直接呼叫该 Skill 的名称或使用
/smyx-elderly-bed-exit-wandering-monitoring-analysis触发 - 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 the "elderly-bed-exit-wandering-monitoring-analysis" skill.
- Provides real-time detection of bed exit, prolonged wandering, and extended periods of immobility for elderly persons at night using video analysis.
- Designed for safety monitoring in nursing homes and for seniors living alone, with automated tiered alerts for abnormal behaviors.
- Enforces strict memory and data access policies, requiring all historical report queries to use the cloud API—local or long-term memory access is strictly forbidden.
- Mandatory open-id acquisition process before any analysis; execution halts without a provided open-id.
- Includes clear steps for video analysis, alert logic, and structured reporting, with historical reports shown in Markdown tables with direct report links.
元数据
常见问题
Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能 是什么?
Identifies abnormal behaviors such as getting out of bed at night, prolonged wandering, and remaining motionless for extended periods. It is suitable for nig... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 77 次。
如何安装 Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install smyx-elderly-bed-exit-wandering-monitoring-analysis」即可一键安装,无需额外配置。
Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能 是免费的吗?
是的,Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能 支持哪些平台?
Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能 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 Elderly Bed-Exit & Wandering Monitor | 老人离床徘徊监测技能?
由 smyx-skills(@18072937735)开发并维护,当前版本 v1.0.0。
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