by 安全扫描
OpenClaw
安全
high confidenceThe skill's code and instructions are internally consistent: it reads a local mounts file, calls a local LLM service to generate timeline JSON, and writes scheduled tracks to a local timeline DB; it does not request unrelated credentials or perform network calls to external endpoints.
评估建议
This skill appears to do what it claims: read an active_hardware_mounts.json, call a local LLM on localhost:1234 to generate timeline JSON, and save tracks to s2_timeline_data/rendered_tracks.json. Before installing, ensure: (1) the local LLM at localhost:1234 is trusted — untrusted LLMs can produce unexpected or malformed JSON; (2) other S2 connectors (e.g., s2-nlp-connector) are the only modules that provide microphone/mmWave/sensor data — the orchestrator itself does not access hardware; (3) ...详细分析 ▾
✓ 用途与能力
The manifest, SKILL.md, and skill.py align: the orchestrator consumes an Active Mounts JSON, uses a local LLM to generate timeline keyframes, and injects the resulting track into a local rendered_tracks.json. There are no unexpected external credentials, unrelated binaries, or config paths requested.
ℹ 指令范围
SKILL.md describes features like microphone monitoring, mmWave sensing, swarm pings and booking actions; the code itself does not access microphones, radar sensors, or external booking APIs — it only reads active_hardware_mounts.json and writes rendered_tracks.json. This is coherent if other S2 modules (e.g., s2-nlp-connector) supply sensor data; confirm those connectors are what provide sensitive inputs rather than this skill directly.
✓ 安装机制
No install spec or external downloads are present; this is an instruction+code skill that runs from included skill.py. No external packages or remote archives are fetched by the skill itself.
✓ 凭证需求
The skill requests no environment variables or credentials. The only network call is to http://localhost:1234 (a local LLM endpoint) which is consistent with the declared behavior. No unrelated secrets or external service credentials are requested.
✓ 持久化与权限
always is false and the skill does not attempt to modify other skills or system-wide agent settings. It writes its own timeline DB under the current working directory (s2_timeline_data/rendered_tracks.json), which is a scoped and expected persistence behavior.
安全有层次，运行前请审查代码。
运行时依赖
无特殊依赖
版本
latestv1.0.02026/3/20
Initial release introducing spatiotemporal orchestration across the 6-Element Matrix. - Predictive timeline rendering: Converts natural language intents into 4D timeline tracks with scheduled keyframes. - Real-time context awareness: Reads active hardware mounts to tailor rendering to available devices. - Bilingual documentation (English/中文) with detailed scenarios for smart home automation, emotional sensing, and multi-room orchestration. - Supports simulated or real devices (recommended: s2-nlp-connector). - Example use cases include post-workout routines, birthday events, emotional health monitoring, pet diagnostics, and elderly care.
● 无害
安装命令 点击复制
官方npx clawhub@latest install s2-timeline-orchestrator
镜像加速npx clawhub@latest install s2-timeline-orchestrator --registry https://cn.clawhub-mirror.com
数据来源：ClawHub ↗ · 中文优化：龙虾技能库
OpenClaw 技能定制 / 插件定制 / 私有工作流定制
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