AI & Machine Learning Skills

Converts text to OpenAI GPT-compatible formats, optimizing content for improved model performance and integration.

Provides LLMs guidance and assistance for building AI and machine learning applications.

This skill generates numerical representations (embeddings) of text data, enabling semantic search and understanding by AI systems.

This skill creates isolated Tauri sidecar processes for running embeddings, enhancing performance and security by offloading heavy computations.

Find relevant documents or data points based on semantic similarity using vector embeddings powered by winsorllc.

This skill intelligently combines multiple embeddings using a dynamic fusion strategy for enhanced AI/ML model performance and accuracy.

Ark Embedding generates vector embeddings for your data, enabling semantic search and similarity analysis across diverse text formats.

This skill generates diverse embedding strategies for optimal semantic understanding and retrieval across various AI models & datasets.

Embeds Python Debugger (pdb) breakpoints directly into executable files for live debugging within running programs – a powerful troubleshooting tool.

Helps with Vercel, AI as part of building AI and machine learning applications workflows.

This Gemini Prompt Helper assists in crafting effective prompts for Gemini models, boosting your AI output quality and streamlining your workflow.

Helps with LLMs, patterns as part of building AI and machine learning applications workflows.

Generates detailed, evocative images inspired by classic Chinese literature and art styles based on text prompts.

The Rag Engineer skill automates RAG (Retrieval-Augmented Generation) pipeline creation & optimization for enhanced LLM performance and knowledge integration.

This Rag implementation facilitates seamless retrieval-augmented generation by efficiently connecting language models to external knowledge sources for richer responses.

Provides LLMs guidance and assistance for building AI and machine learning applications.

This skill uses Retrieval-Augmented Generation (RAG) to efficiently extract and deliver pertinent information based on user queries, boosting accuracy and relevance.

Helps with code generation, video as part of building AI and machine learning applications workflows.

Automates code review & vulnerability scanning of AI-generated code to ensure security best practices are followed.

Helps with image processing, OpenAI as part of building AI and machine learning applications workflows.

Semantic search sidecar for Karakeep bookmarks. Find bookmarks by meaning, not just keywords. Uses vector embeddings for intent-aware retrieval.

Semantic memory search using Qdrant + Ollama embeddings (QRAS). Use as primary method for recalling workspace files, notes, decisions, and prior work before falling back to built-in search.

Rag Architect constructs well-structured documents from retrieved knowledge, streamlining information synthesis and boosting research efficiency.

Helps with AI, code generation as part of building AI and machine learning applications workflows.