Skill UI

This tool collects distributed trace spans to build a comprehensive, visual trace tree of an agent's task execution. It allows users to understand the exact flow, timing, and coordination between multiple agents involved in a task. Use it to identify performance bottlenecks, analyze the critical path, and synthesize a detailed narrative summary of complex multi-step workflows.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

A structured framework for managing complex, iterative, and high-stakes decision processes (e.g., stochastic optimization, ensemble comparison, model rollouts). It mandates the creation of a detailed, auditable 'ledger' that records every prior result, new input, search space metric, and decision mark. This tool enforces rigorous evidence trails and defaults to safe, dry-run modes, ensuring that confidence is built through verifiable, step-by-step proof rather than assumed certainty.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

This tool provides comprehensive safety scanning for untrusted inputs, such as user submissions, API payloads, and webhook data. It actively detects various security threats, including prompt injection, jailbreak attempts, instruction override attacks, and unsafe content. Beyond basic checks, it offers deep analysis, full multi-layer scanning, and even capabilities to train defensive models using detected threats. Essential for secure AI application development.

This skill provides regulator-grade feature attribution for complex trading signals (LSTM/Transformer). By constructing a feature-contribution graph and running single-entry PageRank, it identifies the top-K features that most drove a prediction. This is crucial for compliance with regulations like the EU AI Act and SEC Reg-AI, providing a robust, auditable audit trail for algorithmic trading systems.

This skill generates high-dimensional vector embeddings (384-dim) from text, code, or documents using the ruvector package. It utilizes the ONNX all-MiniLM-L6-v2 model, making it ideal for implementing advanced semantic search, similarity comparison, and data clustering. The resulting vectors are normalized and can be efficiently stored and indexed using HNSW, significantly enhancing retrieval accuracy in RAG and knowledge systems.

A comprehensive vector search plugin offering two distinct paths: large-scale corpus search using HNSW (supporting RaBitQ quantization for memory efficiency) and a fast WASM-backed router for high-priority hot patterns. Ideal for implementing Retrieval-Augmented Generation (RAG), semantic search across massive datasets, or routing queries to specific, limited patterns.

A comprehensive, multi-stage workflow for enhancing and structuring raw footage using advanced AI tools. It covers the entire pipeline—from initial recording (Screen Studio) through AI planning (Claude/Codex), deterministic cutting (FFmpeg), compositing (Remotion), asset generation (ElevenLabs/fal.ai), and final human polish (Descript/CapCut). Designed for users creating vlogs, tutorials, or polished corporate videos.