Skill UI

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.

Scaffolds production-ready generative AI agents on Vertex AI with Gemini models, multi-modal tools, RAG, and Cloud Run/Endpoint deployment, including observability and evaluation for enterprise scenarios.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

This comprehensive guide provides step-by-step instructions for integrating the Google Gemini API into applications using Python and Node.js. It covers the entire lifecycle, from basic text generation to advanced use cases like multimodal inputs (text + image), real-time streaming responses, function calling/tool use, and multi-turn chat. Includes best practices for model selection, error handling, and production deployment.

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.

MolyKit is a comprehensive toolkit designed for building robust, cross-platform AI chat applications. It provides core utilities for integrating large language models (LLMs) like OpenAI, handling real-time SSE streaming responses, and managing complex cross-platform asynchronous patterns (PlatformSend, spawn()). The kit includes ready-to-use widgets for messages, prompt inputs, and avatars, making it ideal for developers creating sophisticated, multilingual chat experiences.

Helps craft precise ir.model.access.csv lines and ir.rule record rules for Odoo, debug access denials, and enforce scoped or multi-company visibility for modules.

This sophisticated framework enables cross-functional 'what-if' scenario modeling. Unlike simple stress tests, it maps compounding adversity across all business domains (Finance, Product, Engineering, etc.). Use it when facing complex, multi-variable risks or making strategic decisions with significant downside potential, allowing teams to map cascading failure paths and define proactive hedges.

Auri is an advanced, next-generation voice assistant built upon Amazon Alexa and the powerful Claude AI model. Unlike traditional assistants, Auri conducts real, contextual conversations, enabling deep reasoning, remembering user context via DynamoDB, and providing natural, empathetic responses in Brazilian Portuguese. It elevates interactions from simple commands to complex, multi-turn dialogues requiring sophisticated natural language understanding.

A comprehensive framework for optimizing inventory levels and planning replenishment in multi-location retail environments. It covers advanced demand forecasting techniques (e.g., Holt-Winters, ML models), calculating safety stock based on variability, estimating promotional uplift, and generating actionable purchase orders while minimizing stockouts and excess inventory. Ideal for professionals managing complex SKU portfolios across various supply chain systems.