llmfit-advisor
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
560 skills found
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
Manage isolated LlamaFarm development environments using git worktrees for parallel agent sessions and service testing.
Enforce strict code quality, correctness, and Rust design patterns for the Turso database, prioritizing data integrity, performance, and maintainable, idiomatic code.
Automates the creation and maintenance of JSDoc documentation for DuploJS utilities, ensuring consistent index.md structures and synchronized code examples.
High-performance Solana meme coin trading for AI agents: sniping, MEV-protected execution, rug detection, and automated position management.
Queen-led multi-agent orchestration for Claude Code, featuring Byzantine consensus, persistent collective memory, and adaptive task distribution for complex software projects.
Standardized workflow and guidelines for Laravel 11/12 application development, including stack detection, dependency management, and integration with Laravel Boost tools.
Manage OpenClaw's built-in Chrome browser and chrome-devtools-mcp integration for robust browser automation using the Model Context Protocol.
A color-coded, real-time context usage progress bar for the Claude Code statusline and manual on-demand checks.
Development guide for creating custom nodes in FlowGram.ai workflows, supporting both auto-generated simple forms and complex custom UI components.
A comprehensive library of 305+ modular instruction packages, Python CLI tools, and agent workflows designed to extend the capabilities of AI coding assistants like Claude Code, Cursor, Aider, and Gemini CLI.
Transform AI agents into proactive partners using WAL Protocol, persistent memory buffers, and autonomous cron scheduling to anticipate needs and improve performance.