全部能力

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing…

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch,…

Search arXiv for academic papers. Use when users want to find research papers, preprints, or academic articles on any topic. Supports filtering by date,…

用于撰写高质量计算机科学研究论文（会议、期刊、学位论文）的综合工具包。提供叙事构建指导、…

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct…

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want…

使用《Scientific…》中的论文架构与写作策略原则，润色、重构学术文本，或将其翻译为符合《Nature》风格的英文。

Automatically generate **professional-grade** structured project Wiki from documentation, code, design files, and images. Use when: - User requests "generate wiki", "create docs", "create documentation" - User requests "update wiki", "rebuild wiki" - User requests "list plugins", "install plugin", "manage plugins" - Project needs automated documentation generation Features: - Smart project structure and tech stack analysis - **Deep code analysis** with semantic understanding - **Mermaid diagrams** for architecture, data flow, dependencies - **Cross-linked documentation** network - Incremental updates (only changed files) - Code blocks link to source files - Multi-language support (zh/en) - **Plugin system for extensions** For Chinese instructions, see references/SKILL.zh.md

Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required.

Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function…

Use when researching Apple frameworks, APIs, or WWDC sessions - provides techniques for retrieving full transcripts, code samples, and documentation using…

Guide to selecting and optimizing embedding models for vector search applications.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices,…

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search,…

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII…

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm,…

Use when the user asks to "research code", "how does X work", "where is Y defined", "who calls Z", "trace code flow", "find usages", "explore this library",…

Use when writing or reviewing k6 documentation across TypeScript types, user docs, and release notes.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training