amo-tech-ai
amo-tech-ai/rocket-path-ai
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/amo-tech-ai/rocket-path-ai --skill framer-motion-animator命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | framer-motion-animator |
| redirect | motion/framer-motion-animator |
| description | Moved to motion/framer-motion-animator. Use that skill instead. |
Moved to motion/framer-motion-animator. All content now lives there.
OpenClaw Gateway setup, configuration, and best practices. Use when installing OpenClaw, configuring channels (WhatsApp/Telegram/Discord), setting up providers (OpenAI/Google/Anthropic), creating skills, managing the gateway daemon, or troubleshooting OpenClaw issues. Triggers on: openclaw, gateway, whatsapp channel, telegram bot, openclaw skill, openclaw config, openclaw install, openclaw cron, openclaw security.
Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning. **This project:** We use OpenAI text-embedding-3-small (1536) and store as vector(1536) in knowledge_chunks. halfvec is an optional future optimization; apply this skill's tuning (ef_search, iterative_scan
基于 SOC 职业分类