charlieviettq

charlieviettq / awesome-agent-skill

Public

Curated skill pack for LLM agents in engineer and science workflow (Cursor & Claude ready).

agent-skills ai-agents automation awesome-list claude-code
12
3
89% credibility
Found May 25, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

awesome-agent-skill is an open-source library of 170+ portable skills for AI coding assistants (Cursor, Claude Code, Codex CLI, and others). These skills are reusable playbooks that give coding agents structured procedures for tasks like handling documents (PDF, Word, Excel), generating scientific visualizations, analyzing data, writing code reviews, running security checks, and browser automation. The project uses a portable folder format where each skill lives in its own directory containing a SKILL.md file that describes when and how to use it. Skills are installed by copying folders into a project's .cursor/skills or .claude/skills directory. The library includes Python scripts for document manipulation, data analysis, machine learning templates, statistical utilities, and AI-powered image generation. It's designed for developers who want their AI coding assistant to handle specialized workflows consistently and correctly rather than starting each task from scratch. The project is MIT-licensed and maintained by an individual developer.

How It Works

1
📚 Discover the skill library

Someone hears about or finds a collection of 170+ reusable skills that make their AI coding assistant smarter and more capable.

2
🔧 Install into your project

You copy the skill folders you need into your project, choosing only the domains that match your work—data, documents, visualizations, security, or all of them.

3
🔄 Reload your AI assistant

After copying the skills, you restart your coding agent session so it can discover and learn about all your new capabilities.

4
🤖 Your assistant now knows more

Your AI coding partner can now help you handle PDFs, build charts, analyze spreadsheets, create scientific diagrams, write code reviews, and dozens of other specialized tasks it couldn't do before.

5
💬 Ask for help in your style

You describe what you need naturally—'create a flowchart showing participant flow' or 'fill in this form'—and your assistant recognizes which skill to use and follows the right process.

6
Your work gets done correctly
📊
For data tasks

Your assistant analyzes your files, checks your data quality, and produces reports or visualizations following scientific standards

📝
For document tasks

Your assistant edits Word files, fills PDF forms, or creates presentations while preserving tracked changes and formatting

🖼️
For visualization tasks

Your assistant generates publication-ready charts, diagrams, or infographics using AI image generation with quality reviews

✅ Everything works just right

Your project has reliable, reusable workflows for common tasks. Your coding assistant does more, faster, with fewer mistakes—ready for your next project too.

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AI-Generated Review

What is awesome-agent-skill?

This is a curated library of 170+ reusable skill packs for LLM coding agents like Cursor and Claude Code. Think of it as a playbook library that gives your AI assistant repeatable workflows for tasks like spec-driven development, security reviews, browser QA, PDF manipulation, and scientific data analysis. The skills are plain-text files that agents can read and follow, eliminating the need to re-explain common workflows every session. Python-based tooling handles installation via shell scripts that copy skills into your project's agent directories.

Why is it gaining traction?

The hook is portability: these skills work across multiple agent platforms with a single conversion step. The bundle installer lets you grab just core-workflow skills or the full stack with one command. For teams doing scientific computing, the built-in support for formats like DOCX, XLSX, PDFs, and bioinformatics files (FASTA, FASTQ) fills a gap that generic agent prompts miss. The autoskill feature even attempts to observe your workflow and propose new skills based on patterns it detects.

Who should use this?

Research scientists working with experimental data who want their coding agent to handle file conversions, statistical analysis, and document generation consistently. Engineering teams standardizing code review or security practices across projects. Developers using Cursor or Claude Code who find themselves repeatedly explaining the same workflows to their agent.

Verdict

With a 0.9% credibility score and only 12 stars, this is a young, unproven project. The documentation is thorough and the skill coverage is genuinely useful for scientific workflows, but the low community engagement means limited real-world testing. Worth watching, but hold off on production dependency until it gains traction.

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