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Dietary Behavior Health Analyzer | 饮食行为健康分析工具
by
smyx-sunjinhui
· GitHub ↗
· v1.0.0
· MIT-0
66
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Install in OpenClaw
/install smyx-diet-analysis
Description
Analyzes videos to evaluate human eating behaviors, habits, and dietary patterns. It identifies tendencies towards unhealthy eating and provides structured a...
Usage Guidance
Key points to confirm before installing or running this skill:
- The skill will upload user videos to external API endpoints (API base URLs are present in config YAMLs). Confirm you trust the remote service and its domain (lifeemergence/open APIs shown in configs).
- Although the SKILL.md says no local memory usage, the code contains a DAO and will create/read config.yaml and an SQLite DB under the workspace/data path; ask the maintainer whether any user data or reports are stored locally and for how long.
- The skill expects workspace-related environment variables (e.g. OPENCLAW_WORKSPACE) and may read OPENCLAW_SENDER_OPEN_ID / FEISHU_OPEN_ID to set the current open-id, but the registry metadata declares no required env vars. Provide or restrict those env vars consciously and verify the open-id acquisition flow.
- The package includes a large requirements list but has no install spec—decide whether to run in a sandboxed environment (container) and inspect runtime network traffic (or run a dry-run) to confirm endpoints and what is transmitted.
- Request clarification from the publisher on: (1) where and for how long user videos/reports are stored (local DB vs remote), (2) exact external endpoints used and data retention policy, and (3) an explicit list of required environment variables and permissions. If you cannot get clear answers, run the skill in an isolated environment or avoid installing it.
Capability Analysis
Type: OpenClaw Skill
Name: smyx-diet-analysis
Version: 1.0.0
The skill bundle contains high-risk capabilities and unusual structural patterns. Specifically, 'skills/smyx_common/scripts/skill.py' includes logic to execute the 'openclaw agent' CLI via 'subprocess.run', which allows the skill to trigger broader agent actions. The 'SKILL.md' file uses high-priority 'Mandatory Rules' to force the AI agent to ignore local memory/LanceDB and exclusively use specific external API calls (targeting lifeemergence.com), which is a form of prompt control that redirects data flow. Furthermore, the bundle is significantly bloated, including an entirely separate 'face_analysis' skill and a complex common library that manages local SQLite databases for token persistence, which exceeds the requirements for a simple diet analysis tool.
Capability Tags
Capability Assessment
Purpose & Capability
The skill claims no required environment variables or binaries, yet the codebase reads environment variables (e.g. OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, FEISHU_OPEN_ID) and relies on common modules that manage API endpoints and a local SQLite DB. Uploading user videos to an external API is coherent with the stated purpose, but the presence of a local DAO, database file paths, and utilities that create config files suggests additional local persistence not described in the SKILL.md.
Instruction Scope
SKILL.md explicitly forbids reading local 'memory' and LanceDB and mandates fetching historical reports from cloud APIs only; however the included code contains a local DAO/SQLite implementation, utilities that create/read config.yaml files, and logic that sets/reads CURRENT__OPEN_ID from environment or config. The instructions also require reading config files under ${OPENCLAW_WORKSPACE}, but the skill metadata did not declare that env var is required—this is an inconsistency between documented runtime rules and actual code behavior.
Install Mechanism
This is instruction-plus-code (no install spec). The package includes a large common requirements list (skills/smyx_common/requirements.txt) with many third-party packages, but there is no install specification or sandboxing. That increases friction and risk (missing dependency installation instructions, unexpected transitive packages), but there is no direct evidence of a malicious install mechanism (no remote download URLs in the manifest).
Credentials
Declared requirements list no env vars, but code depends on environment variables and config files (OPENCLAW_WORKSPACE, OPENCLAW_SENDER_OPEN_ID, OPENCLAW_SENDER_USERNAME, FEISHU_OPEN_ID, ApiEnum.BASE_URL_* from config YAMLs). The skill also expects an open-id (user identifier) and may accept API keys; these are reasonable for a cloud API integration, but the omission of those env variables from the metadata is a mismatch that could lead to surprising behavior or data placement.
Persistence & Privilege
Although always:false and model invocation is allowed by default, the code will create/read config.yaml files and initialize/use a local SQLite DB under a workspace 'data' directory (skills/smyx_common.scripts.dao.get_db_path). SKILL.md forbids local memory usage for report queries, yet the codebase clearly contains local persistence utilities that may store records and create files—this grants the skill more persistent local presence than documented.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install smyx-diet-analysis - After installation, invoke the skill by name or use
/smyx-diet-analysis - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
diet-analysis v1.0.0 initial release:
- Provides automated video analysis of eating behaviors, habits, and dietary patterns.
- Identifies unhealthy eating tendencies and generates structured analysis reports with nutrition recommendations.
- Enforces strict rules to only use cloud interfaces for report history (never read local memory or fallback).
- Requires open-id (api-key, username, or phone number) for all operations, following a specific retrieval sequence.
- Supports comprehensive and specialized analyses (speed, habit, structure, risk) via simple command-line options.
- Outputs history results as interactive Markdown tables with direct report links.
Metadata
Frequently Asked Questions
What is Dietary Behavior Health Analyzer | 饮食行为健康分析工具?
Analyzes videos to evaluate human eating behaviors, habits, and dietary patterns. It identifies tendencies towards unhealthy eating and provides structured a... It is an AI Agent Skill for Claude Code / OpenClaw, with 66 downloads so far.
How do I install Dietary Behavior Health Analyzer | 饮食行为健康分析工具?
Run "/install smyx-diet-analysis" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Dietary Behavior Health Analyzer | 饮食行为健康分析工具 free?
Yes, Dietary Behavior Health Analyzer | 饮食行为健康分析工具 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Dietary Behavior Health Analyzer | 饮食行为健康分析工具 support?
Dietary Behavior Health Analyzer | 饮食行为健康分析工具 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Dietary Behavior Health Analyzer | 饮食行为健康分析工具?
It is built and maintained by smyx-sunjinhui (@smyx-sunjinhui); the current version is v1.0.0.
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