Stop Guessing. Find Agent Skills That Actually Work.
Turn “Will it install? Will it work? Is it safe?” into an audit summary and an install-ready setup guide.
Copy install command / JSON snippet → restart Claude Desktop
Stop Guessing. Start from Audited Agent Skills.
These skills either reached S‑tier in our audit, or consistently score high across clarity, usefulness, output quality, maintainability, and novelty.
pytorch
S“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”
agno
S“It promises to be the Kubernetes for agents, but let's see if developers have the patience to learn yet another orchestration layer.”
nuxt-skills
S“It's essentially a well-organized cheat sheet that turns your AI assistant into a Nuxt framework parrot.”
systematic-debugging
S“This skill is essentially a stern rubber duck that yells 'Did you read the error message?' before you can even ask for help.”
mcp-builder
S“This guide is so comprehensive it might just teach the AI to write its own MCP servers and put you out of a job.”
What are Agent Skills, and how do they relate to MCP?
MCP (Model Context Protocol) is the open standard that lets agents like Claude securely talk to tools and data sources. It defines how a client discovers servers, exchanges messages, and manages permissions.
Agent Skills are concrete, ready-to-use MCP servers and configurations. Think of MCP as the “socket” standard, and Agent Skills as the specific power adapters you plug in — file browsers, Git clients, ticket systems, custom APIs, and more.
Why is finding Claude Skills so painful?
Searching “Claude Skill” on GitHub gives you a wall of repos. You open README after README, guess compatibility, and hunt for install steps. Installing and getting one skill to actually run often takes 15–30 minutes — and one failure means starting over.
- Choice paralysis + high trial costEvaluating a repo takes minutes; installing and debugging takes 15–30 minutes. One wrong pick wastes real time.
- No Unified StandardWithout comparable criteria, you’re forced to trust vibes and luck — you only learn after you try.
- Information OverloadLong docs, scattered examples, missing steps. Finding the one command you need shouldn’t be a scavenger hunt.
- Stars ≠ reliabilityA popular repo can be outdated, scenario-specific, or simply not runnable in your setup.
Why Developers Choose MCPxel
We do the messy work upfront: organize key information, normalize install steps, and give you a consistent rating before you install.
Trusted Agent Skills in 3 Steps
From discovery to integration, without the guesswork.
Platform Features
Built for the Agentic Future. Engineered for Trust.
DeepSeek V3 Evaluation Core
Automated code review engine that scores 5 dimensions: Clarity, Utility, Completeness, Maintainability, and Novelty.
Security Risk Warnings
AI-powered analysis to highlight potential security concerns mentioned in documentation or code patterns.
Capability Search
Powered by structured data. Find 'tools that can read PDF invoices' even if the README never says those exact words.
Continuous Tracking
Ratings are based on specific GitHub commits. We aim to keep our reviews synchronized with major repository updates.
Standardized Metrics
S/A/B/C/D tiering system based on objective engineering standards, reducing decision paralysis.
Open Registry Initiative
Building a transparent, community-driven metadata collection for the MCP ecosystem.
The Ecosystem is Booming
New tools appear every day — and so do new pitfalls.
Indexed Skills
Growing
always updating
Time Saved
Less
trial & error
Decision Quality
More
confidence
Frequently Asked Questions
Got questions? We've got answers.
What is MCP?
The Model Context Protocol (MCP) is a standard for connecting AI models to external data and tools. It allows agents like Claude to securely interact with your local files, databases, and APIs.
How do you rate skills?
We use an automated AI agent (powered by DeepSeek V3) that analyzes the README, code structure, and documentation clarity. We look for clear installation instructions, security best practices, and active maintenance.
Is it free?
Yes! MCPxel is free to browse. For now, submissions are internal while we build automated crawling and rating.
Can I submit my skill?
Not publicly yet. Right now we curate and ingest skills internally (and via crawler) to keep quality consistent during MVP.
Do you support private repos?
Not yet. Currently we only support public GitHub repositories to ensure transparency for the community. We are working on a secure way to audit private repos.
How often are ratings updated?
We re-evaluate skills whenever there are significant updates to the repository or upon user request. You can also manually trigger a re-audit from the skill detail page.
Ready to enhance your Agent?
Browse the collection and start installing.