# Results for "claude code skills and plugins"

> AI-ranked search results for `claude code skills and plugins` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 115 total matches; showing the top 3.

Explore on the web: https://awesome-repositories.com/q/claude-code-skills-and-plugins

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## Results

- [anthropics/skills](https://awesome-repositories.com/repository/anthropics-skills.md) (151,506 ⭐) — This project provides a standardized framework for extending the functional range of artificial intelligence agents through a registry of modular, declarative instructions. It enables agentic workflow automation by allowing developers to define task-specific behaviors and operational constraints that guide how agents interact with external tools and execute multi-step processes.

The system distinguishes itself through a directory-based discovery model and a plugin-registry architecture that facilitates the distribution of specialized workflows. By utilizing a schema-driven specification that
- [juliusbrussee/caveman](https://awesome-repositories.com/repository/juliusbrussee-caveman.md) (73,390 ⭐) — Caveman is a set of tools and configurations designed for large language model token optimization. It focuses on reducing the amount of data processed during AI interactions to lower costs and maximize the available context window.

The project implements a fragmented communication style that replaces full grammatical sentences with concise technical keywords. This approach extends to AI context optimization by condensing memory files and tool descriptions, and includes a specialized configuration for generating terse, one-line code reviews and short conventional commit messages.

The system i
- [addyosmani/agent-skills](https://awesome-repositories.com/repository/addyosmani-agent-skills.md) (60,849 ⭐) — Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists.

The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated