Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
AI Employee Team
v1.0.1AI员工协作技能包 - 多AI角色配置、任务自动分配、进度监控、结果汇报。适合想要自动化运营的团队。
⭐ 0· 393·5 current·5 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
SKILL.md/README describe a multi-component AI team framework (task dispatcher, status monitor, report generator, meeting coordinator, templates, package.json, npm start, etc.), but the bundle only contains one script (scripts/employee-manager.js) plus docs. The claimed capabilities exceed what's actually provided.
Instruction Scope
Runtime instructions tell the user to run 'npm install' and 'npm start', edit config files, and use many CLI commands and endpoints that are not present in the supplied files. The SKILL.md expects a working project layout (package.json, many scripts) that is missing — following the docs could fail or run unknown code if additional files are fetched later.
Install Mechanism
There is no formal install spec (lowest-risk). However SKILL.md instructs running 'npm install' and 'npm start' even though package.json is not included in the bundle. That mismatch is suspicious: either required files were omitted from the package or the docs are outdated.
Credentials
The skill requests no environment variables or credentials (proportionate). SKILL.md references using LLM models (e.g., 'gpt-4') and private deployment/pricing but does not declare any API keys or integration details — missing required credentials for real LLM use.
Persistence & Privilege
The skill does local file I/O (creates config/ and memory/ directories and writes memory files). 'always' is false and there are no instructions to modify other skills or system-wide settings. Local persistence is expected for the described memory feature.
What to consider before installing
This package appears inconsistent: the docs promise a full orchestration system but the archive only contains a single employee-manager script and docs. Before installing or running anything: 1) ask the publisher for the full source or an upstream repository (package.json and the other scripts) and verify integrity; 2) inspect any package.json and all scripts that would be installed by 'npm install' (look for network calls, exec/spawn, remote downloads); 3) run in an isolated/sandbox environment (or container) if you must test; 4) be cautious about supplying LLM API keys — the skill references models but does not declare how keys are used or stored; 5) treat local memory files under memory/ as potentially sensitive and review read/write behavior. The current artifact is more likely incomplete or poorly packaged than overtly malicious, but the mismatches warrant caution.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.