Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF,...

Generate PowerPoint presentations with slides, layouts, charts, and multimedia

Guides creation of Product Requirements Prompts (PRPs) - comprehensive requirement documents that serve as the foundation for AI-assisted development

Best practices for writing production Go code. Use when writing, reviewing, or refactoring Go code. Covers error handling, concurrency, naming conventions, testing patterns, performance...

Scan lyrics for explicit content, verify explicit flags match actual content

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Strunk & White composition review using the 11 principles from "Elements of Style" Chapter II. Use when analyzing structure, improving flow, or tightening prose.

LaTeX 模板高保真优化器，支持任意 LaTeX 模板的样式参数对齐、标题文字对齐、标题格式对比（加粗）、HTML 可视化报告、LaTeX 自动修复建议和像素级 PDF 对比验证

Scan lyrics for pronunciation risks, prevent Suno mispronunciations

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Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.

Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.

Optimize content for search engines with keyword analysis, readability scoring, meta descriptions, and competitor comparison. Use this when users want to improve SEO, optimize blog posts, or...

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Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or...

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or...