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The Semantic Translation Bridge
by
MilesXiang
· GitHub ↗
· v1.1.0
· MIT-0
154
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0
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Active Installs
2
Versions
Install in OpenClaw
/install s2-classic-scene-parser
Description
Translates rigid scene triggers into rich 6-element spatial intents for smart home control with manual override and personalized mode roaming.
Usage Guidance
This package is largely a local simulator that creates and reads files in whatever directory you run it from. Before running: 1) review skill.py yourself (it is short and readable); 2) run it in an isolated/empty directory or in a container/VM to avoid accidentally creating or overwriting s2_* files or s2_chronos.db in important locations (S2_ROOT = os.getcwd()); 3) note that the 'roaming' and 'permission stripping' claims are narrative: the code only sets local variables and prints status, it does not contact external services or change real hotel systems; 4) if you plan to integrate this with a real S2 ecosystem or networked agents, perform a security review for network calls or auth handling at that integration boundary; 5) do not run as root/admin unless you understand the implications.
Capability Analysis
Type: OpenClaw Skill
Name: s2-classic-scene-parser
Version: 1.1.0
The skill bundle implements a semantic parser for smart home scenes within a fictional 'S2' ecosystem. The Python code (skill.py) performs local file operations and manages a SQLite database to simulate 'Avatar Roaming' and scene logging. It uses only standard libraries, lacks any network activity or suspicious execution patterns, and its instructions in SKILL.md are consistent with the provided simulation logic without attempting to subvert the agent's behavior.
Capability Assessment
Purpose & Capability
Name/description (semantic bridge / avatar roaming) match what the package actually does: translate scene names into textual '6-element' intents, merge local avatar habits, write timeline tracks, and log mandates to a local SQLite DB. No unrelated credentials or binaries are requested.
Instruction Scope
SKILL.md instructs the user to run python skill.py and explains the simulated IPC steps. The instructions and code are interactive and will create/read/write files (avatar_habits.json, house_topology.json, rendered_tracks.json, s2_chronos.db) in the current working directory. The README language is grandiose (claims to 'strip hotel AI agent permissions'), but the implementation only simulates that behavior locally—there is no external network/permission escalation in the code.
Install Mechanism
No install spec; this is instruction + code only. No external downloads, package installs, or archive extraction are performed by the skill.
Credentials
The skill requests no environment variables or credentials. All state is stored in local files under the current working directory; no secrets are requested or used.
Persistence & Privilege
always:false and no autonomous elevation. The skill persists data by creating directories, JSON files, and an SQLite DB in os.getcwd(); this is expected for a simulator but means running it in an important filesystem location could overwrite or add files. It does not modify other skills or system-wide settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install s2-classic-scene-parser - After installation, invoke the skill by name or use
/s2-classic-scene-parser - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
**Major update: S2-Classic-Scene-Parser elevated from a translation script to the central OS message bus.**
- Now functions as the core communication and orchestration bus for all S2 OS modules, routing scene triggers through Avatar memory, physical topology, 4D timeline orchestration, and causality database logging.
- Expanded documentation with a detailed 4-step inter-process communication (IPC) cascade and a comprehensive breakdown of bus orchestration across phases.
- Enhanced "Avatar Roaming" feature allows cloud-based personal habits to override local settings, enabling your preferences to take effect across different locations and hardware vendors.
- The 20-classic-scenes semantic matrix is further expanded, precisely detailing each translation from rigid scene modes to 6-element intents.
- Introduced a self-healing developer experience: auto-generates mock data, topologies, and databases if previous S2 phases are not present, ensuring a seamless demonstration and testing environment.
- Focus shifted from just semantic parsing to demonstrating industry-leading, invisible, and user-personalized smart control across multi-agent environments.
v1.0.0
s2-classic-scene-parser v1.0.0
- Initial release of the scene semantic translation engine for S2 Spatial-Primitive OS.
- Translates classic rigid scene triggers into 6-element spatial intents for improved flexibility.
- Natively supports 20+ industry-standard smart home and hospitality scenes with bilingual (English/中文) documentation.
- Provides guidance for users, developers, and manufacturers to enable semantic scene adaptation and hardware decoupling.
- Recommends modern practices for wall panel integration via universal JSON payloads, ensuring future-proof scene control.
Metadata
Frequently Asked Questions
What is The Semantic Translation Bridge?
Translates rigid scene triggers into rich 6-element spatial intents for smart home control with manual override and personalized mode roaming. It is an AI Agent Skill for Claude Code / OpenClaw, with 154 downloads so far.
How do I install The Semantic Translation Bridge?
Run "/install s2-classic-scene-parser" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is The Semantic Translation Bridge free?
Yes, The Semantic Translation Bridge is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does The Semantic Translation Bridge support?
The Semantic Translation Bridge is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created The Semantic Translation Bridge?
It is built and maintained by MilesXiang (@spacesq); the current version is v1.1.0.
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