Skill UI

Build, run, and visualize multi-step AI generation workflows via muapi.ai. Translate natural language intents into runnable node graphs, discover or edit existing pipelines, and execute them interactively or through the CLI with automated monitoring.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

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.

This skill outlines the development of an automated pipeline to ingest raw Indicators of Compromise (IOCs) from various sources. It performs normalization, defanging (e.g., converting 'http://' to 'hxxp://'), and converts indicators into the standardized STIX 2.1 format. The processed, safe indicators can then be securely distributed via TAXII feeds, MISP, and other threat intelligence platforms.

This comprehensive skill automates the entire process of generating novel, robust, and benchmark-grounded research ideas in robotics and embodied AI. It guides users from a broad initial concept to concrete, actionable project proposals by chaining rigorous literature surveys, novelty verification, and critical reviews. The workflow enforces robotics constraints, prioritizing simulation-first, falsifiable, and feasible real-world applications.

This robust skill manages the entire academic research lifecycle, automating processes from initial idea discovery and literature review to experimental execution, rigorous cross-model review, and final paper drafting. Ideal for researchers who need a comprehensive, autonomous workflow to turn a broad research direction into a polished document, supporting complex parameters like resume capability and varying review difficulties. Use when needing full pipeline automation.

Trivy is a comprehensive open-source vulnerability scanner used for detecting vulnerabilities, misconfigurations, secrets, and license violations across OS packages, language dependencies, and container images. It integrates seamlessly into CI/CD pipelines for robust security testing and compliance.

A foresight agent that runs a rigorous 12-step pipeline combining Claude’s qualitative intelligence with Python’s deterministic arithmetic to analyze high-stakes future scenarios, infer reasonable horizons, and surface quantitative signals for prediction requests.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

AuraKit converts a single /aura command into a secure full-stack Claude pipeline with 34 modes, OWASP SEC-01~15 safeguards, runtime hooks, tiered models, token savings, and multilingual reviewers.

A multi-layered defense system designed to detect sophisticated prompt injection attacks targeting LLM-powered applications. It combines regex pattern matching for known signatures, heuristic scoring for structural anomalies, and advanced DeBERTa-based classification. Ideal for building robust input validation layers, securing RAG pipelines, and performing proactive AI security scanning.

Run Hugging Face models directly in JavaScript/TypeScript for text, vision, and speech tasks without backend servers, using Transformers.js in Node.js or modern browsers with WebGPU, quantization, and pipeline abstractions for efficient inference.