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Found 5,649 Skills
Scans the codebase to generate project-doc.md and AGENTS.md. Runs a full scan on first use and a smart delta scan on subsequent runs. Uses understand-anything + context-mode when available, falls back to native tools otherwise. Only updates AGENTS.md on detected architectural changes with human confirmation.
基于极目数据的亚马逊商品发掘与潜力爆品挖掘。当用户提到产品挖掘、潜力爆品、高转化选品、点击增长分析、市场增长机会、关键词选品、FBA利润筛选、细分市场商品发掘、卖家来源筛选、product mining, potential bestsellers, high-conversion product selection, market growth opportunities, Jiimore data, FBA profitability screening, keyword-based product selection时触发此技能。即使用户未明确提及"极目"，只要其需求涉及基于转化率、点击量和利润指标的亚马逊关键词驱动选品，也应触发此技能。
Advances the validator execution state baseline without running checks for requests such as "skip validator", "advance validator baseline", or "mark current tree as validated without running checks".
Build or update a professional-grade design system library in Figma from a codebase. Useful for keeping the Figma source of truth in sync with shipped components.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
This skill should be used when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Use for Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Complete testing setup with Neon database branching, Playwright browser tests, integration tests, and unit tests. Isolated branches with automatic TTL cleanup.
Instantly provision production-ready Postgres databases with Neon Instagres. Use when setting up databases, when users mention PostgreSQL/Postgres, database setup, or need a development database. Works with Drizzle, Prisma, raw SQL.
For discovering and understanding database structure, tables, columns, and relationships
Control Notion via Python SDK. TRIGGERS - Notion API, create page, query database, add blocks.
Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".