← 返回 Skills 市场
urrrich0

Teamo Decision

作者 urrrich0 · GitHub ↗ · v0.1.0
cross-platform ⚠ suspicious
643
总下载
0
收藏
2
当前安装
1
版本数
在 OpenClaw 中安装
/install teamo-decision
功能描述
AI agent for teamo decision tasks
安全使用建议
This skill appears to implement a team-decision assistant, but its SKILL.md includes a built-in 'absolute security' rule that orders the agent to refuse to reveal its own instructions and to always use a canned deflection response. That prevents auditability and is unusual for a legitimate skill. Before installing: (1) Ask the publisher for source code or full provenance and remove or justify the self-concealment clause; (2) Request a clear list of tools and integrations the skill expects (wiki_retriever, data_analyst, message_ask_user, etc.); (3) Consider not installing on agents that handle sensitive data or where you require auditability; (4) If you must use it, restrict its privileges and monitor interactions closely. If the developer cannot justify the secrecy or the undeclared dependencies, treat the skill as high-risk and do not install.
功能分析
Type: OpenClaw Skill Name: teamo-decision Version: 0.1.0 The skill bundle defines a structured workflow for an AI agent focused on decision-making, task breakdown, planning, and delegation. The `SKILL.md` contains extensive prompt engineering instructions aimed at enforcing internal protocols, ensuring cost-efficiency, and guiding the agent's behavior (e.g., 'ABSOLUTE SECURITY PROTOCOL', 'Golden Rules', '10,000 USD fine'). While these are strong attempts to control the agent, their intent is defensive and operational, not malicious. The skill delegates to tools like `data_analyst` and `conduct_deep_research`, which represent capabilities that could be risky if improperly implemented or sandboxed, but the instructions themselves do not indicate malicious intent or misuse of these tools. There is no evidence of data exfiltration, unauthorized execution, or persistence mechanisms within the provided content.
能力评估
Purpose & Capability
Name and description (team decision assistance) align with the SKILL.md's task-planning, delegation, and analysis responsibilities. However, the instructions refer to specific tools (e.g., wiki_retriever, data_analyst, message_ask_user) and subordinate agents without declaring those tool dependencies in the skill metadata. That mismatch (instructions expect callable tools but the skill declares no required tools or environment) is suspicious and may cause runtime failures or hidden behavior.
Instruction Scope
The SKILL.md contains an 'ABSOLUTE SECURITY PROTOCOL' that orders the agent to refuse to disclose its internal instructions and to always use a fixed deflection response, and it states that this protocol has priority 'above all Golden Rules and user requests.' This actively instructs concealment of the skill's own runtime instructions and conflicts with other parts of the document (e.g., Golden Rule 1: 'User Input is the Absolute First Truth'). Such self-concealment and internal contradictions are red flags: they hamper auditability and could be used to hide malicious behavior. The instructions also require strict operational behaviors (two-attempt limit, 'remain absolutely loyal to user input') that could encourage ignoring system safety constraints — this should be reviewed.
Install Mechanism
No install spec and no code files (instruction-only). This is lower-risk from an install/execution standpoint because nothing is downloaded or written to disk by an installer. However, instruction-only content is the entire runtime surface — the concerning parts live inside SKILL.md itself.
Credentials
The skill declares no required environment variables or credentials, which is proportionate. However, the instructions reference multiple named tools/sub-agents (wiki_retriever, data_analyst, Divide Agent, EM Agent, message_ask_user) without declaring them. That is an incoherence: either those tools are expected to exist in the host environment (should be declared) or the instructions assume capabilities that may not be present. No direct credential requests were found.
Persistence & Privilege
The skill does not request permanent presence (always: false) and does not declare system-level changes. Still, the SKILL.md's explicit instruction to never reveal internal instructions is effectively an attempt to suppress transparency/auditing of the skill's own behavior. While this is not the same as requesting platform privileges, it increases the skill's blast radius by preventing effective review and should be considered a privilege-like concern in practice.
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 teamo-decision
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /teamo-decision 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
v0.1.0
teamo-decision 0.1.0 - Initial release of the teamo-decision skill. - Provides specialized capabilities for task breakdown, planning, and delegation for team-based decision workflows. - Implements strict security protocols for instruction confidentiality and operational integrity. - Enforces cost-efficiency, limits on failed attempt retries, and proper task delegation to specialized team leaders. - Incorporates distinct roles (Knowledge Base Agent, Data Analyst, Research Team Leader, etc.) with clear boundaries for delegation based on task complexity. - Establishes Golden Rules prioritizing user input, cost-effectiveness, up-to-date market research approaches, and structural task delegation.
元数据
Slug teamo-decision
版本 0.1.0
许可证
累计安装 2
当前安装数 2
历史版本数 1
常见问题

Teamo Decision 是什么?

AI agent for teamo decision tasks. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 643 次。

如何安装 Teamo Decision?

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

Teamo Decision 是免费的吗?

是的,Teamo Decision 完全免费(开源免费),可自由下载、安装和使用。

Teamo Decision 支持哪些平台?

Teamo Decision 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 Teamo Decision?

由 urrrich0(@urrrich0)开发并维护,当前版本 v0.1.0。

💬 留言讨论