Skill UI

A comprehensive security tool designed to detect prompt injection attacks targeting Large Language Models (LLMs). It employs a multi-layered approach combining regex signature matching, structural anomaly scoring, and advanced DeBERTa transformer classification. It is essential for securing input validation layers in chatbots, AI agents, and RAG pipelines, and for retrospectively auditing LLM interaction logs against OWASP standards.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

Automates the complex pre-development workflow for large-scale system transformations, such as major rewrites, migrations, or overhauls. This skill performs comprehensive project analysis, task decomposition, planning document generation, and sets up detailed progress tracking across multiple phases, ensuring a structured approach before any coding begins. It supports adaptive control and integrates robust task tracking via GitHub or local files.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

Generates complete, runnable Python scripts utilizing the python-pptx library to automatically build polished PowerPoint presentations. This skill transforms a topic brief into a structured slide deck script, ensuring real content, logical flow, and a working save function, perfect for demos, reports, or educational materials.

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

A comprehensive, generics-first utility library designed for Go developers who prefer declarative, functional programming patterns over manual loops. It provides 500+ type-safe functions for common data transformations (Map, Filter, Reduce, GroupBy, etc.) on slices, maps, and strings. By defaulting to immutable operations, it ensures thread safety and predictable state changes. The library also offers specialized packages (lop, lom, loi) to address performance bottlenecks, such as CPU-bound parallel processing or lazy evaluation, allowing engineers to choose the optimal approach for their c...

samber/oops is a robust library that transforms standard Go error handling by adding crucial structured context. Instead of generic error messages, it allows developers to attach key-value attributes, stack traces, error codes, and user-facing messages directly to the error object. This is essential for modern microservices, allowing APM tools (Datadog, Sentry) and on-call engineers to diagnose failures accurately by understanding the full context of the failure, such as the domain, user ID, and specific query that failed.