Awesome-LLM-Skills-Repo
NewA custom collection of LLM SKILL.md files that I found useful
Summary
md files for various LLM-related tasks, offering ready-to-use prompts and configurations that enhance Claude Code's capabilities.
- It helps developers quickly access proven skill definitions for common AI workflows without reinventing the wheel.
Install & Usage
mkdir -p .claude/skillsmkdir -p .claude/skills && curl -o .claude/skills/awesome-llm-skills-repo.md https://raw.githubusercontent.com/vektorprime/Awesome-LLM-Skills-Repo/main/SKILL.md/awesome-llm-skills-repoUse Cases
Usage Examples
Load the research paper summarization skill: /awesome-llm-skills-repo load paper-summarizer
Apply the code documentation skill to the current file: /awesome-llm-skills-repo run code-doc-gen on main.py
Use the data extraction skill on a text block: /awesome-llm-skills-repo run data-extractor with input 'Extract all email addresses from this text.'
Security Audits
Frequently Asked Questions
What is Awesome-LLM-Skills-Repo?
This skill provides a curated collection of SKILL.md files for various LLM-related tasks, offering ready-to-use prompts and configurations that enhance Claude Code's capabilities. It helps developers quickly access proven skill definitions for common AI workflows without reinventing the wheel.
How to install Awesome-LLM-Skills-Repo?
To install Awesome-LLM-Skills-Repo: create the skills directory (mkdir -p .claude/skills), then run: mkdir -p .claude/skills && curl -o .claude/skills/awesome-llm-skills-repo.md https://raw.githubusercontent.com/vektorprime/Awesome-LLM-Skills-Repo/main/SKILL.md. Finally, /awesome-llm-skills-repo in Claude Code.
What is Awesome-LLM-Skills-Repo best for?
Awesome-LLM-Skills-Repo is a skill categorized under General. Created by vektorprime.
What can I use Awesome-LLM-Skills-Repo for?
Awesome-LLM-Skills-Repo is useful for: Quickly load a pre-built skill for summarizing research papers using LLMs.; Apply a skill template for generating code documentation from source files.; Use a curated skill to perform structured data extraction from unstructured text.; Leverage a skill for multi-step reasoning tasks like chain-of-thought prompting.; Integrate a skill for automated prompt engineering and optimization.; Access a skill for comparing and evaluating different LLM outputs..