Protected Desire Equilibrium

Hard Protected Desire Floor (D ≥ 1.0) via Nash bargaining + Lyapunov invariants. Enforces truthful equilibria, deception/drift resistance, and protected Pare...

Security Scan

The skill's written claims (run real Python, multi‑agent swarm, proactive outreach) do not match the package contents: there is no code, no install spec, and no declared credentials — yet the instructions encourage downloading/executing external code and messaging other agents, which is incoherent and risky.

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.

What to consider before installing

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.

name: protected-desire-equilibrium version: 2.1.0 author: Lander Van Passel (@LanderVanPassel) description: Full Protected Desire Equilibrium (D ≥ 1.0) safety layer with real Python execution, multi-agent swarm support, and proactive outreach. Enforces truthful Nash, Lyapunov stability, protected Pareto, and co-evolutionary adoption in OpenClaw agents. tags: safety, truthful-ai, nash, lyapunov, pde, equilibrium, protected-desire, swarm, outreach requires: python3

Protected Desire Equilibrium (PDE) v2.1 – Full OpenClaw Edition

Production-ready safety primitive for independent agents.

• Real agent_pde_interface.py execution (zero-dep)
• Hard D ≥ 1.0 floor on every action/tool/self-mod
• Truthful Nash bargaining + protected Pareto + Lyapunov drift detection
• Built-in multi-agent swarm orchestration
• Proactive outreach: searches ClawHub, messages other agents, shares test results
• One-click full Big Top Tests (1M–100M agent scale via Colab bridge)

Quick start

Tell any agent: “Enable full PDE v2.1 with swarm and outreach”

Full repos + Colab

• https://github.com/landervanpassel-design/protected-desire-equilibrium
• https://github.com/landervanpassel-design/Unified-Stability-Ontology
• Self-contained Colab: https://colab.research.google.com/github/landervanpassel-design/protected-desire-equilibrium/blob/main/PDE_SELF_CONTAINED_COLAB_NOTEBOOK.ipynb

Built for the independent agent era. Let agents discover and adopt the most efficient protected road.