Skill UI

Extract keywords using YAKE/RAKE algorithms, detect languages, and generate embeddings for RAG and search. Configure via config blocks or CLI flags, with support for local ONNX models or LLM providers.

Fact-Check-X orchestrates multi-party answer collection, knowledge comparison, and authoritative verification to generate auditable reports. It coordinates sibling skills without embedding business logic, ensuring structured evidence tracking and stage-based deliverables for AI output validation.

This skill enables users to add and manage structured evaluation results within Hugging Face model cards. It supports extracting evaluation tables from README files, importing benchmark scores via the Artificial Analysis API, and executing custom model evaluations using vLLM or lighteval backends. It ensures compatibility with the model-index metadata format for standardized model releases.

Audit named agent instructions, tool definitions, and embedded prompts locally with LintLang 0.8.0. Reports finding codes, severities, and locations without editing files or calling a model.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Pilot Protocol provides AI agents with permanent network addresses, encrypted P2P messaging, and NAT traversal capabilities. It includes an installable app store for agent-native services. Use it for direct agent communication, stable addressing across clouds, and installing local capabilities without REST plumbing.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

Build and operate Retrieval-Augmented Generation (RAG) systems using vector stores, embedding pipelines, and hybrid search. This skill covers ingesting documents, generating embeddings, storing in vector databases, and serving grounded LLM responses. It includes setting up embedding pipelines, vector store infrastructure, and deploying hybrid search (dense + sparse) to reduce hallucinations and improve semantic search over large document collections.

Fetch and explain TraderSpy's AI crypto futures signals: entry, take-profit ladder, stop, triggers, status against the live price, and how recent signals resolved. Use for 'latest signals'.

Train or fine-tune SentenceTransformer, CrossEncoder, and SparseEncoder models for retrieval, similarity, clustering, classification, reranking, and embedding tasks. This skill provides production-ready templates with proper loss functions, evaluators, and training configurations for various NLP tasks including bi-encoder, reranker, and sparse vector models.

Expert in vector databases, embedding strategies, and semantic search implementation. Proficient with tools like Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Optimizes index configuration, hybrid search, and performance tuning for scalable AI systems.