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.
440 skills found
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
A CTF solver agent that performs triage on challenges, identifies the vulnerability category, and routes tasks to specialized skills for web, pwn, crypto, forensic, and reverse engineering analysis.
An automated visual note and flowchart generator. Converts text or keywords into styled diagrams, mind maps, and handwritten notes exported as images without requiring file-reading permissions.
Build complete UI screens by composing multiple uxscii components. Use when you need to create, scaffold, or build .uxm screens like login, dashboard, profile, settings, or checkout pages.
Optimize agent context windows through KV-caching, observation masking, summarization-based compaction, and context partitioning to reduce costs and latency.
Build production-ready, reusable Terraform modules for multi-cloud infrastructure. Includes standardized patterns for AWS, Azure, GCP, and OCI with built-in testing and validation.
Analyzes codebases to generate hierarchical documentation, onboarding guides, and architectural mapping, helping teams understand and document their projects efficiently.
Automated migration workflow from legacy Crowi (Express/Swig) to modern architecture (Next.js 16/Fastify/ts-rest).
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
Guided, systematic feature development agent that orchestrates codebase exploration, architectural design, implementation, and automated testing.
Extract YouTube video subtitles or transcripts directly into local text files using yt-dlp or browser automation.
Clarify ambiguous requirements through systematic dialogue and scoring to ensure high-quality, actionable PRDs before starting implementation.