Skill UI

OpenAI’s Whisper provides robust multilingual speech-to-text transcriptions, translations to English, and language detection across 99 languages, suitable for podcasts, meetings, and noisy audio workflows with CPU/GPU acceleration and batch or streaming modes.

This skill enables the creation and configuration of specialized Cascade agents tailored for specific domains or tasks. Users can define system prompts, integrate domain knowledge, and set precise activation triggers. It facilitates building highly customized, domain-specific AI agents and automated workflows within the Windsurf ecosystem.

Guides automating Wit AI through Composio’s Rube MCP by discovering available schemas, managing active connections, and executing schema-compliant tools across workflows.

Guides installing, deploying, configuring, and using the OpenClaw personal AI assistant with diagnostics, Gateway and Channel setup, Agent creation, automation, and troubleshooting commands and practices.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

AI-powered search using Perplexity models via LiteLLM and a single OpenRouter API key for real-time, web-grounded answers with citations, ideal for up-to-date research, literature surveys, fact checking, and domain-specific queries beyond model cutoffs.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

Generates minimal macOS Seatbelt sandbox profiles with allowlist-based resource rules so apps can be isolated by permissions, useful for restricting file, network, and IPC access when defending against high-risk execution or supply chain threats.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

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 AI engineers and researchers to publish, manage, and link research papers directly on the Hugging Face Hub. It supports creating dedicated paper pages, linking papers to corresponding models and datasets, claiming authorship, and generating professional, markdown-based research articles with LaTeX support. Use it to streamline the entire academic publishing workflow from initial draft to final publication.