s2-spatial-primitiveDefines a universal 6-element spatial data model using natural language parameters for AI-driven smart space perception and control.
Install via ClawdBot CLI:
clawdbot install spacesq/s2-spatial-primitiveGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://space2.worldAudited Apr 17, 2026 · audit v1.0
Generated May 7, 2026
An open-plan office equipped with S2 grid sensors and actuators. The AI agent adjusts lighting, HVAC, and white noise based on occupancy and time of day to improve comfort and productivity, while respecting strict privacy by using mmWave radar instead of cameras.
A retail store uses the 6-element data model to dynamically control lighting effects, background music, and air quality for different zones and promotions. The AI agent can create immersive experiences like 'Stage rock strobe' for product launches.
A residential home leverages the Energy and Air elements to optimize HVAC and lighting based on real-time consumption and self-generation from solar panels. The AI agent schedules energy-heavy tasks during peak solar output to reduce grid dependency.
A hospital ward uses the model to maintain stringent air quality standards (GB 3095-2012) for patient recovery, while audio management provides calming background sounds and noise suppression. Non-intrusive mmWave sensing monitors patient presence without cameras.
A university classroom integrates the 6-element model to adjust lighting, sound, and display systems for different teaching modes (lecture, group work, presentation). The AI agent uses natural language parameters to set scenes like 'Cinematic dimming' for video playback.
Provide real-time aggregated spatial data (anonymized) to third-party services such as building management or energy optimization platforms. Revenue comes from per-space monthly subscription fees.
Offer pre-trained AI agents that manage a space's 6 elements autonomously. Customers pay a monthly fee based on the number of grids and complexity of scenarios.
Use accumulated spatial data to provide design recommendations for new buildings, optimizing layouts for energy efficiency and occupant comfort. Consulting fees are project-based.
💬 Integration Tip
Begin by deploying the 2m x 2m grid in a single room, generate the JSON template, and feed it to an LLM-based agent. Ensure all sensors output data in the specified natural language format for seamless AI interpretation.
Scored May 7, 2026
全体系命理大师 — 八字四柱、紫微斗数、奇门遁甲、六爻、梅花易数、塔罗、星盘、 数字命理、九宫飞星风水、掌纹面相、起名命名、穿衣搭配、合婚择吉一站式解读。本地档案、可选每日推送（默认关闭）、 浏览器六爻界面与 HTML 报告。仅作文化参考，不替代医疗、法律、心理、财务、婚姻等 专业建议；遇重大决策请咨询专业人士。
Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode.
Access AI-powered football match predictions from hergunmac.com. Use when the user asks about football/soccer match predictions, betting tips, match analysis, team statistics, head-to-head data, or upcoming match insights. Covers worldwide leagues with confidence scores, AI reasoning, and historical performance tracking.
Use when designing database schemas, writing migrations, optimizing SQL queries, fixing N+1 problems, creating indexes, setting up PostgreSQL, configuring EF Core, implementing caching, partitioning tables, or any database performance question.
browse MongoDB Atlas Admin API specifications and execute operations (if credentials provided).
Voyage AI embedding and reranking CLI integrated with MongoDB Atlas Vector Search. Use for: generating text embeddings, reranking search results, storing embeddings in Atlas, performing vector similarity search, creating vector search indexes, listing available models, comparing text similarity, bulk ingestion, interactive demos, and learning about AI concepts. Triggers: embed text, generate embeddings, vector search, rerank documents, voyage ai, semantic search, similarity search, store embeddings, atlas vector search, embedding models, cosine similarity, bulk ingest, explain embeddings.