Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Data sourced from ClawHub. View on ClawSkills
Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install pytorch-patternsOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install pytorch-patternsβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/pytorch-patterns/π‘Extract and place the folder at the path above, then restart your agent.
What pytorch-patterns can do for your AI workflow
Head-to-head comparison directly from your Claude conversation
Native Claude integration via the open Model Context Protocol (MCP)
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing pytorch-patterns
Help me get started with pytorch-patterns
Explains what pytorch-patterns does, walks through the setup, and runs a quick demo based on your current project
Use pytorch-patterns to head-to-head comparison of coding agents (Claude Code, Aider, Codex...
Invokes pytorch-patterns with the right parameters and returns the result directly in the conversation
What can I do with pytorch-patterns in my ai agent workflow?
Lists the top use cases for pytorch-patterns, with example commands for each scenario
Guides & tutorials for AI skills
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Claude comes with built-in capabilities, but MCP skills extend it in ways the base model can't. Here's a clear breakdown of what each type of tool is good for, with real examples of when to reach for a skill versus relying on Claude's native abilities.
pytorch-patterns extends your AI assistant with the ability to head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using pytorch-patterns as its underlying capability.
pytorch-patterns integrates with Claude through the Model Context Protocol (MCP) standard. The skill is version-controlled and shareable β if you work with a team, you can commit the skill to your project repo and everyone gets the same capability without any extra setup.
Getting started with pytorch-patterns takes about two minutes. Place the skill at `~/.claude/skills/pytorch-patterns/` (personal, all projects) or `.claude/skills/pytorch-patterns/` (project-specific), then restart your AI client. From that point, typing `/pytorch-patterns` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
pytorch-patterns is a community-contributed AI productivity skill available through DiscoverAISkills. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/pytorch-patterns/ for personal use (all projects), or .claude/skills/pytorch-patterns/ for project-specific use. Restart your AI client, then invoke with /pytorch-patterns or let the AI discover it automatically.
pytorch-patterns supports Claude. It integrates seamlessly with these AI platforms to extend their capabilities.
pytorch-patterns is free to install. Check the repository for licensing information.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Platforms
Automate my ai agent tasks using pytorch-patterns
Identifies repetitive steps in your workflow and sets up pytorch-patterns to handle them automatically