Skill UI

This assistant provides comprehensive performance profiling for complex computing systems. It analyzes various targets, including Python scripts, running processes, GPU utilization, and memory bottlenecks. Utilizing both external tools (like nvidia-smi, cProfile) and advanced code instrumentation, it measures CPU, memory, and interconnect performance, generating a structured report with actionable insights and a mandatory changelog of all modifications.

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.

This meta-skill acts as a comprehensive Learning Science Coach, teaching users *how* to learn effectively rather than just *what* to study. It draws upon advanced cognitive science principles—including Spaced Repetition, Active Recall, Metacognition, and Bloom's Taxonomy—to diagnose learning gaps, optimize study methods, and build structured, long-term memory retention systems. Ideal for students, professionals, and lifelong learners seeking systematic methods to accelerate knowledge acquisition.

A universal infrastructure for automatically evolving and optimizing AI agents across any domain. Using LLM-driven mutation and iterative solve-observe-evolve cycles, it enhances critical components like prompts, skills, and memory by benchmarking performance against measurable datasets, enabling the creation of self-improving AI systems.

This guide provides essential knowledge on resource limitations within WebContainer environments (such as memory, CPU, file system size, and process count). It is crucial for developers utilizing the WebContainers API or StackBlitz SDK to understand these constraints. Learn best practices for monitoring memory usage, optimizing mounted file systems by excluding unnecessary assets (like test or documentation folders), and managing dependencies to prevent common issues like Out-Of-Memory (OOM) crashes and slow build times.

A comprehensive playbook detailing advanced techniques for post-exploitation security bypass on Linux systems. Covers escaping restricted shells (rbash), evading noexec/read-only filesystems using in-memory execution (DDexec, memfd_create), and circumventing mandatory access controls like AppArmor and SELinux. Essential for penetration testing and red team operations.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

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.

This tool provides a comprehensive overview of your project's memory usage across all associated memory systems. It reports metrics such as line counts, topic file counts, capacity limits, stale entries, and actionable recommendations to help maintain memory integrity and optimize performance.