Skill UI

Analyzes text to determine emotional tone and polarity, classifying passages as positive, negative, or neutral; ideal for processing reviews, social media posts, surveys, or any feedback that requires opinion mining or emotion detection.

Enables Claude to analyze text with the nlp-text-analyzer plugin, delivering sentiment, keyword, and topic insights for customer feedback, articles, or reports that need NLP understanding.

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

This guide details how to set up the Speak language learning platform integration. It covers installing necessary SDKs (Speak SDK, OpenAI), configuring API keys, and establishing the connection required for sophisticated AI-powered tutoring. Use this when building applications that require real-time speech recognition, pronunciation feedback, and multilingual language support.

This skill orchestrates deep critical reviews of research work using external AI models like Codex, Gemini, or Claude. It gathers project context, identifies logical gaps, and provides adversarial feedback on methodologies and results. Suitable for academic papers, experimental validation, and refining research ideas through multi-round reasoning.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

This skill guides the entire lifecycle of repeatable AI agent loops. Use it to discover recurring work patterns within codebases, find established solutions in a library, or interview users to craft bounded, effective loops. It supports auditing, repairing, running, and preparing AI workflows for publication, treating the loop as a robust feedback system.

A sophisticated multi-agent development harness inspired by GANs. It separates product planning, code generation, and rigorous evaluation into three distinct agents (Planner, Generator, Evaluator). This creates an adversarial feedback loop, allowing projects to iteratively reach professional, production-grade quality far beyond what a single-agent workflow can achieve. Ideal for complex, full-stack applications.

Explains AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review handling, author feedback, and stage-by-stage planning for submissions.

This guide details the complex, multi-stage review process for top-tier AI conferences like IJCAI. It covers strategies for Phase 1 summary rejection, managing conflicts of interest, understanding the roles of decision actors (PC, AC, SAC), and optimizing paper clarity to maximize acceptance chances. Use this when planning a submission strategy or interpreting reviewer feedback.

An advanced, AI-assisted tool for generating high-quality multiple-choice questions (MCQs) suitable for various academic levels. It supports both light, gamified quizzes and deep, evaluative assessments with detailed feedback. The system ensures factual accuracy through rigorous document research and allows export to major platforms like Moodle, Kahoot!, and Wooclap.