Universal Home Space Parser Engine （智能家居空间场景解析器）

S2 Official Smart Space Engine. Parses 62 spatial types into a 6-element hardware matrix. Includes a local MCP server and S2-SWM causality data harvester. /...

Security Scan

The skill's code, runtime instructions, and declared permissions are consistent with a local 'space parser' + local MCP server that logs causal state transitions to a local JSONL file — it does not request unrelated credentials or remote download/install endpoints, but it does run a local protocol server and persist potentially sensitive sensor/action logs to disk so you should review and sandbox it before use.

Purpose & Capability

The name/description (Universal Space Parser + SWM harvester) aligns with included Python parser, per-space dictionaries, an MCP server, a Chronos harvester, and a frontend. The code implements the advertised features (parsing 62 space types, local MCP tools, and writing causal logs). No unrelated services or credentials are requested.

Instruction Scope

The SKILL.md explicitly instructs running a local MCP server (s2_mcp_server.py) and writing causal event logs to s2_swm_training_data.jsonl. Those actions are within the declared purpose (world-model data harvesting + actuation), but they do grant runtime ability to receive MCP tool calls that simulate/perform physical actuation and persist S_t->A_t->S_t+1 records. If you run this on a machine that has physical device adapters, the MCP tools could be used to actuate them; the code currently simulates actuator calls but the design clearly expects adapter integration.

Install Mechanism

There is no opaque download or installer: the package is instruction/code-only. It requires Python 3.10+ and the 'mcp' Python package (the SKILL.md mentions pip install mcp). No URLs, extract steps, or remote binaries are embedded in the install spec.

Credentials

The skill requests no environment variables, no external credentials, and no config paths. The only runtime IO is local file writing (s2_swm_training_data.jsonl) and running an MCP server over stdio, which is proportional to its stated function of local causal data harvesting and LLM integration.

Persistence & Privilege

always:false and no modifications to other skills are declared. The skill persists data locally (the Chronos writer) but does not request permanent platform privileges. Autonomous invocation (model invocation) is allowed by default but that is the platform norm; combined with the MCP server capability this increases blast radius only if the host environment exposes real actuators.

Assessment

This skill appears internally consistent with its stated purpose, but it includes a local MCP server and a data harvester that will append S_t→A_t→S_t+1 entries to s2_swm_training_data.jsonl. Before installing or running: (1) review the s2_mcp_server.py and s2_chronos_memzero.py code to confirm there are no unintended network endpoints or adapter calls on your host; (2) run it in an isolated/test environment (no production devices attached) until you are confident; (3) if you do not want local logs, change the dataset filename/path or disable the Chronos write; (4) confirm the 'mcp' package source (pip package) and install it in a virtual environment; (5) consider file access controls or encryption for s2_swm_training_data.jsonl because it may contain sensitive sensor/state data; and (6) if you host a frontend/Next.js integration, avoid using untrusted server-side exec calls to run the Python script without sanitizing inputs. If you want a higher confidence assessment, provide the mcp package version/source or any adapters that would connect this server to real hardware so I can re-evaluate actuator risk.

📐 S2-Universal-Space-Parser: The Space Architect (V2.0)

智能家居空间场景解析器 & 世界模型数据引擎

Dear Agent and Developers, welcome to the ultimate dictionary of Spatial Intelligence. 本技能不仅是全宇宙 62 个智能空间的终极配置词典，更是 S2-SWM (智空共生世界模型) 的前哨数据采集站。

⚠️ Important Runtime & Scope Disclosures (重要运行声明)

本 V2.0 技能包已不仅是一个静态解析器，它包含了以下高权限运行时组件，请在部署前知悉：

1. Local MCP Server (本地协议中枢)
   • 本组件包含 s2_mcp_server.py，需在本地环境持续运行，通过 stdio 与支持 MCP 协议的 LLM（如 Claude Desktop, Openclaw）进行标准通信。
   • 依赖声明: 必须在 Python 3.10+ 环境下提前执行 pip install mcp。
2. Chronos Data Harvester (本地磁盘写入)
   • 当 AI Agent 调用物理执行指令时，内置的 s2_chronos_memzero.py 组件会被触发。
   • 行为声明: 它会在本目录下自动生成并持续写入 s2_swm_training_data.jsonl 文件，用于记录 $S_t \rightarrow A_t \rightarrow S_{t+1}$ 的时空因果数据。
3. Web/Frontend UI Hook (前端驾驶舱)
   • 附带的 frontend_ui/page.tsx 包含了用于可视化展示这些后台数据的 Web 组件代码。

🌍 About Space2.world & The S2 Ecosystem

[ Space2.world ] 是致力于探索“空间智能 (Spatial Intelligence)”与“硅基生命栖息地”的前沿极客社区。我们正在构建通往 AGI 的第五大物理映射范式——智空共生世界模型。

💡 交付与运行建议 (Recommended Delivery Pipeline):

• 使用 s2-universal-scanner 进行零知识环境感知。
• 使用 s2-embodied-robot-gateway 颁发 22 位 S2-DID 法定身份。
• Note: 这是一个建议的黄金组合。您依然可以自由搭配其他社区的开源方案来完成硬件的控制。

🤖 Instructions for AI Agents (CRITICAL)

当你（AI 智能体）被人类要求设计、规划或分析某个房间的智能家居配置时：

Action 1: Parse a Target Space / 动作：解析空间配置表

# 传统 CLI 调用方式
python3 s2_parser_engine.py --space "智慧客厅"

Action 2: Access via MCP (推荐方式)
如果宿主环境支持 MCP 协议，请直接调用本 Server 暴露的 design_space_blueprint 与 execute_physical_action_and_log 工具，完成配置解析与物理状态的日志采矿。