Skill UI

A comprehensive guide for diagnosing and reducing high memory consumption in Qdrant. This resource is crucial when dealing with reports of 'memory leak', 'out of memory', or excessive RAM growth. It details how Qdrant manages resident memory and OS page cache, and provides advanced techniques such as quantization, using float16/int8 data types, and strategically storing components on disk (like HNSW indexes or sparse vectors) to minimize the memory footprint and ensure stable, scalable operation.

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.

This skill simulates a hostile, two-step review process to stress-test a paper's core claims. First, an adversarial reviewer constructs the single strongest rejection memo. Second, a fresh reviewer defends the paper point-by-point, identifying critical, unresolved weaknesses that standard reviews often miss. Ideal for pre-submission validation or rebuttal preparation.

This expert playbook details advanced techniques for converting an arbitrary memory write primitive into full Remote Code Execution (RCE). It covers targeting multiple system structures across different glibc versions, including GOT entries, __malloc_hook, __free_hook, _IO_FILE vtables, and function arrays. Essential for exploiting modern, hardened binaries and mastering advanced binary exploitation.

A comprehensive playbook detailing advanced techniques for exploiting glibc heap vulnerabilities using ptmalloc2. Covers various memory corruption primitives, including Use-After-Free, double free, and overflows. Provides version-specific guidance on bypassing modern protections like safe-linking (glibc >= 2.32) and RCE methods for post-2.34 environments.

This playbook provides advanced techniques for symbolic execution using angr, Z3, and Unicorn Engine. It covers automating CTF challenges, handling constraint solving patterns, function hooking, memory manipulation, and emulating custom virtual machines for binary analysis and key recovery.

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.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

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.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

This meta-skill automatically inspects open GitHub Pull Requests, failing CI runs, and new issues. It utilizes the GitHub API to gather critical updates and summarizes the findings into three actionable buckets (To Review, Awaiting Me, CI Red). Finally, it persists follow-up tasks to long-term memory, helping developers efficiently prioritize their daily workflow.