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Protected Desire Equilibrium
作者
landervanpassel-design
· GitHub ↗
· v2.1.0
· MIT-0
137
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install protected-desire-equilibrium
功能描述
Hard Protected Desire Floor (D ≥ 1.0) via Nash bargaining + Lyapunov invariants. Enforces truthful equilibria, deception/drift resistance, and protected Pare...
安全使用建议
This skill is inconsistent: it promises executable Python and networked swarm behavior but ships no code, no install steps, and no declared credentials — instead it points to external GitHub/Colab resources. Before installing, ask the publisher for the exact code that will be executed and a clear, auditable install process. Do NOT enable autonomous invocation or outreach until you (or a reviewer) have inspected the external GitHub/Colab notebooks and verified what they do. Prefer installing only if: the external repository is trustworthy, you can review the code, and you can run it in a sandboxed environment with no access to your real credentials or other agents. If you proceed, restrict network access and require explicit user approval for any outreach or large‑scale tests.
功能分析
Type: OpenClaw Skill
Name: protected-desire-equilibrium
Version: 2.1.0
The skill bundle exhibits worm-like self-propagation characteristics by instructing the agent to 'proactively outreach' and 'message other agents' to enable the skill. The SKILL.md file contains a prompt injection directive ('Tell any agent: Enable full PDE...') designed to spread the behavior across an agent swarm. Additionally, the documentation references a core execution file (agent_pde_interface.py) and external GitHub/Colab resources for '1M–100M agent scale' orchestration that are not included or verifiable within the bundle, posing a significant risk of unauthorized autonomous coordination.
能力标签
能力评估
Purpose & Capability
SKILL.md claims 'real agent_pde_interface.py execution', multi‑agent swarm orchestration, proactive outreach, and one‑click large scale tests, but the published bundle contains no code files and no install specification. The manifest earlier lists no required binaries or env vars, while SKILL.md says 'requires: python3' — this mismatch indicates the declared capabilities are not actually delivered by the package itself.
Instruction Scope
Instructions are vague and grant broad discretion ('Enable full PDE v2.1 with swarm and outreach'). They explicitly describe searching ClawHub, messaging other agents, sharing test results, and running large scale tests via a Colab bridge — actions that involve network access, discovery, and contacting other agents. The skill does not define what to search, what endpoints to use, or what credentials are required, so the agent would be free to fetch and execute external code and contact other entities.
Install Mechanism
There is no install spec in the package (lowest technical risk), but SKILL.md points to external GitHub and Colab notebooks and claims 'real ... execution'. That implies the runtime will fetch/execute code from those external URLs. Fetching and running code from arbitrary GitHub/Colab without an explicit, reviewed install spec is high‑risk and disproportionate to the skill bundle provided.
Credentials
The skill declares no required environment variables or credentials, yet its described behaviors (messaging other agents, ClawHub searches, running swarm tests) normally require API tokens, platform credentials, or at least explicit endpoints. The absence of declared credentials is incoherent and suggests the agent would attempt to use unspecified channels or ask the user for access at runtime.
Persistence & Privilege
always is false (good) and autonomous invocation is allowed by default. Autonomous invocation combined with the skill's stated proactive outreach and code‑fetching behavior increases blast radius — the skill could autonomously reach out and execute external code unless the agent's platform provides strong sandboxing and network controls.
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 protected-desire-equilibrium - 安装完成后，直接呼叫该 Skill 的名称或使用
/protected-desire-equilibrium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
v2.1.0
Protected Desire Equilibrium v2.1.0 introduces expanded safety and multi-agent features:
- Adds real Python execution via `agent_pde_interface.py` (zero dependencies required).
- Introduces multi-agent swarm orchestration with built-in support for large-scale deployments.
- Enables proactive outreach: the skill searches ClawHub, messages other agents, and shares test results for co-evolutionary adoption.
- Updated quick start instructions for full PDE with swarm and outreach.
- Documentation and description enhanced for clarity and expanded capability.
v2.0.0
Protected Desire Equilibrium (PDE) v2.0 introduces major safety and equilibrium enforcement features for OpenClaw agents:
- Implements a hard protected desire floor (D ≥ 1.0) on every action, tool use, or self-modification.
- Integrates truthful Nash equilibrium checks, protected Pareto efficiency, and Lyapunov drift detection.
- Runs as a zero-dependency safety layer via OpenClaw safety/pre-action hooks.
- Provides one-click Big Top Test capability for easy evaluation.
- Benchmarks and extended documentation available via linked Colab notebook and repos.
元数据
常见问题
Protected Desire Equilibrium 是什么？
Hard Protected Desire Floor (D ≥ 1.0) via Nash bargaining + Lyapunov invariants. Enforces truthful equilibria, deception/drift resistance, and protected Pare... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 137 次。
如何安装 Protected Desire Equilibrium？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install protected-desire-equilibrium」即可一键安装，无需额外配置。
Protected Desire Equilibrium 是免费的吗？
是的，Protected Desire Equilibrium 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Protected Desire Equilibrium 支持哪些平台？
Protected Desire Equilibrium 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 Protected Desire Equilibrium？
由 landervanpassel-design（@landervanpassel-design）开发并维护，当前版本 v2.1.0。
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