Skill UI

Automatically scans code for reflected, stored, and DOM-based XSS risks across HTML, JavaScript, CSS, and URLs, providing reports and safe proof-of-concept payloads for use in reviews, audits, or pre-deployment checks.

Sentence Transformers provides 5000+ pre-trained models that generate sentence, text, and image embeddings for RAG, semantic search, clustering, and multilingual production workloads without relying on external APIs.

A specialized methodology for reviewing APIs, configuration schemas, and interfaces to identify 'footguns'—design choices where the path of least resistance leads to insecurity. This analysis focuses on making secure usage the default, covering topics like dangerous defaults, primitive vs. semantic type safety, and insecure algorithm selection. It is crucial for architects and reviewers to prevent developer misuse at the design level.

Automates Slack messaging, search, channel management, reactions, and threads via Rube MCP (Composio), enabling scripted posts, queries, and workspace ops once RUBE_SEARCH_TOOLS and Slack connections are active.

Assists in business automation by guiding users through slack bot creator tasks, offering best practices, auto-generated code, and validation for workflows, email, and spreadsheet processes.

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

Automated guidance for Teams Webhook Sender scenarios within business automation, offering best-practice patterns, ready-to-use configurations, and validation steps for workflow, email, spreadsheet, and process optimization tasks.

Tracks CPU, memory, disk/network I/O, DB connections, and threads via the resource-usage-tracker plugin to reveal performance bottlenecks, recommend right-sizing, and guide cost-saving actions.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

This guide establishes a fast, reproducible local development workflow for GPU workloads using Vast.ai. It covers key practices such as testing Docker images locally (in CPU mode), mocking external APIs like Vast.ai, and implementing robust unit and integration tests. This is essential for minimizing cloud GPU costs and accelerating the iteration cycle for complex AI/ML projects.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.