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OpenSkills

31 October 2025·385 words·2 mins

GitHub AI Agent Open Source Typescript AI

Articoli Interessanti - This article is part of a series.

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openskills repository preview

#### Source

Type: GitHub Repository Original Link: https://github.com/numman-ali/openskills Publication Date: 2025-10-31

Summary
#

WHAT - OpenSkills is a universal skills loader for AI coding agents, written in TypeScript. It allows for the installation, management, and synchronization of skills from GitHub repositories, replicating the skills system of Claude Code.

WHY - It is relevant for AI business because it allows for the extension of AI coding agents’ capabilities, improving their effectiveness and flexibility. It solves the problem of having a compatible and easily installable skills system for various AI agents.

WHO - The main actors are the project author, numman-ali, and the community of developers contributing to the project. Indirect competitors include other skill management platforms for AI agents.

WHERE - It positions itself in the market of tools for AI agent development, offering a solution for skill management compatible with various AI coding agents.

WHEN - It is a relatively new project, with initial growth in popularity (347 stars on GitHub). The temporal trend suggests growth potential, but it is still in the maturation phase.

BUSINESS IMPACT:

Opportunities: Integration with our existing stack to enhance AI agents’ capabilities. Possibility of creating a marketplace for proprietary skills.
Risks: Competition with proprietary skill management solutions. Dependence on external repositories for skill installation.
Integration: Possible integration with existing AI agents to extend their functionalities.

TECHNICAL SUMMARY:

Core technology stack: TypeScript, CLI, GitHub API, vitest for testing.
Scalability and architectural limits: Good scalability thanks to the use of TypeScript and GitHub API. Potential limits related to the management of a large number of skills and dependence on external repositories.
Key technical differentiators: Compatibility with the skills system of Claude Code, support for installation from any GitHub repository, skill management via CLI.

Use Cases
#

Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Development Acceleration: Reduction of project time-to-market
Strategic Intelligence: Input for technological roadmap
Competitive Analysis: Monitoring AI ecosystem

Resources
#

Original Links
#

OpenSkills - Original link

Article suggested and selected by the Human Technology eXcellence team, elaborated through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-10-31 07:33 Original source: https://github.com/numman-ali/openskills

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Articoli Interessanti - This article is part of a series.

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - GVCLab/PersonaLive: PersonaLive! : Expressive Portrait Image Animation for Live Streaming

Part : GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

Part : GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

Part : GitHub - yichuan-w/LEANN: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

Part : GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

Part : LLMRouter - LLMRouter

Part : Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,

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Part : Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

Part : GitHub - Tencent-Hunyuan/HunyuanOCR

Part : Effective harnesses for long-running agents Anthropic

Part : GitHub - pixeltable/pixeltable: Pixeltable — Data Infrastructure providing a declarative, incremental approach for multimodal AI workloads

Part : AI Explained - Stanford Research Paper.pdf - Google Drive

Part : We present Olmo 3, our next family of fully open, leading language models

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Part : Presentations — Benedict Evans

Part : Nano Banana Pro: Gemini 3 Pro Image model from Google DeepMind

Part : GitHub - GibsonAI/Memori: Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

Part : GitHub Projects Community (@GithubProjects) on X

Part : I’m starting to get into a habit of reading everything (blogs, articles, book chapters,…) with LLMs

Part : Love this framing！ This is exactly what we’re building at Weco: - you write an eval script (your verifier) - Weco iterates on the code to optimize it against that eval Software 1

Part : Supercharge your OCR Pipelines with Open Models

Part : [2511.09030] Solving a Million-Step LLM Task with Zero Errors

Part : [2511.10395] AgentEvolver: Towards Efficient Self-Evolving Agent System

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Part : Use Cases | Claude

Part : Improving frontend design through Skills | Claude

Part : Sim: Open-source platform to build and deploy AI agent workflows

Part : Context Retrieval for AI Agents across Apps & Databases

Part : said we should delete tokenizers

Part : You Should Write An Agent · The Fly Blog

Part : "🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here"

Part : Link to the Strix GitHub repo: (don't forget to star 🌟)

Part : Source: Thanks and Bharat for showing the world you can in fact tra...

Part : This Claude Code prompt literally turns Claude Code into ultrathink...

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Part : Tongyi DeepResearch: A New Era of Open-Source AI Researchers | Tongyi DeepResearch

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Part : Game Theory | Open Yale Courses

Part : DeepSeek-OCR

Part : Airbyte: The Leading Data Integration Platform for ETL/ELT Pipelines

Part : Enterprise Deep Research

Part : I quite like the new DeepSeek-OCR paper

Part : olmOCR 2: Unit test rewards for document OCR | Ai2

Part : We used DeepSeek OCR to extract every dataset from tables/charts ac...

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Part : DeepSeek OCR - More than OCR - YouTube

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Part : Production RAG: what I learned from processing 5M+ documents

Part : Stanford's ALL FREE Courses [2024 & 2025] ❯ CS230 - Deep Learni...

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Part : PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : nanochat

Part : ROMA: Recursive Open Meta-Agents

Part : NeuTTS Air

Part : Cua: Open-source infrastructure for Computer-Use Agents

Part : MCP Analytics and Authentication Platform

Part : My trick for getting consistent classification from LLMs

Part : If you're late to the whole "memory in AI agents" topic like me, I recommend investing 43 minutes to watch this video

Part : DeepLearning.AI: Start or Advance Your Career in AI

Part : Claude Code best practices | Code w/ Claude - YouTube

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Part : Prompt Packs | OpenAI Academy

Part : AI-Researcher: Autonomous Scientific Innovation

Part : Context Engineering for AI Agents: Lessons from Building Manus

Part : AgenticSeek: Private, Local Manus Alternative

Part : Learn Your Way

Part : Qwen-Image-Edit-2509: Multi-Image Support，Improved Consistency

Part : Qwen-Image

Part : Introducing Tongyi Deep Research

Part : 💾🎉 copyparty

Part : AI Engineering Hub

Part : Deep Chat

Part : ibm-granite/granite-docling-258M · Hugging Face

Part : Google just dropped an ace 64-page guide on building AI Agents

Part : opcode - The Elegant Desktop Companion for Claude Code

Part : NocoDB Cloud

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Part : MemoRAG: Moving Towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery

Part : Enable AI to control your browser 🤖

Part : Total monthly distance traveled by passengers in California’s driverless taxis - Our World in Data

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Part : Huge AI market opportunity in 2025

Part : The Anthropic Economic Index Anthropic

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Part : PaddleOCR

Part : DeepSite v2 - a Hugging Face Space by enzostvs

Part : How to Use Claude Code Subagents to Parallelize Development

Part : Show HN: CLAVIER-36 – A programming environment for generative music

Part : Small models are the future of agentic ai

Part : Kimi K2: Open Agentic Intelligence

Part : Introducing Qwen3-Max-Preview (Instruct)

Part : Scientific Paper Agent with LangGraph

Part : Anthropic's Interactive Prompt Engineering Tutorial

Part : swiss-ai/Apertus-70B-2509 · Hugging Face

Part : Making a font of my handwriting · Chameth.com

Part : SurfSense

Part : LoRAX: Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

Part : NextChat

Part : The LLM Red Teaming Framework

Part : Colette - ci ricorda molto Kotaemon

Part : VibeVoice: A Frontier Open-Source Text-to-Speech Model

Part : [2502.12110] A-MEM: Agentic Memory for LLM Agents

Part : [2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Part : Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS

Part : HumanLayer

Part : PageIndex: Document Index for Reasoning-based RAG

Part : Deploying DeepSeek on 96 H100 GPUs

Part : Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

Part : DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning

Part : [2508.15126] aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

Part : Alexander Kruel - Links for 2025-08-24

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Part : Tiledesk Design Studio

Part : Build a Large Language Model (From Scratch)

Part : Data Formulator: Create Rich Visualizations with AI

Part : browser-use/web-ui

Part : Casper Capital - 100 AI Tools You Can’t Ignore in 2025...

Part : CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents

Part : Show HN: Whispering – Open-source, local-first dictation you can trust

Part : Fallinorg v1.0.0-beta

Part : paperetl

Part : Automatically annotate papers using LLMs

Part : My AI Had Already Fixed the Code Before I Saw It

Part : Llama-Scan: Convert PDFs to Text W Local LLMs

Part : Claudia – Desktop companion for Claude code

Part : Show HN: Fallinorg - Offline Mac app that organizes files by meaning

Part : Focalboard

Part : Elysia: Agentic Framework Powered by Decision Trees

Part : LangExtract

Part : +1 for "context engineering" over "prompt engineering"

Part : The race for LLM cognitive core

Part : [2507.07935] Working with AI: Measuring the Occupational Implications of Generative AI

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Prava - Teaching GPT‑5 to use a computer

Part : InstaVM - Secure Code Execution Platform

Part : Litestar is worth a look

Part : Jobs at Kaizen | Y Combinator

Part : Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

Part : Introducing pay per crawl: Enabling content owners to charge AI crawlers for access

Part : Agentic Design Patterns - Documenti Google

Part : [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Part : Qwen3-Coder: Agentic coding in the world

Part : FutureHouse Platform

Part : Voxtral | Mistral AI

Part : Research Agent with Gemini 2.5 Pro and LlamaIndex | Gemini API | Google AI for Developers

Part : AI Act, c'è il codice di condotta per un approccio responsabile e facilitato per le Pmi - Cyber Security 360

Part : [2507.06398] Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI

Part : MindsDB, an AI Data Solution - MindsDB

Part : Backlog.md – Markdown-native Task Manager and Kanban visualizer for any Git repo

Part : Opencode: AI coding agent, built for the terminal

Part : The new skill in AI is not prompting, it's context engineering

Part : SymbolicAI: A neuro-symbolic perspective on LLMs

Part : Gemini for Google Workspace Prompting Guide 101

Part : Judge Rules Training AI on Copyrighted Works Is Fair Use, Agentic Biology Evolves, and more...

Part : MCP is eating the world—and it's here to stay

Part : How Dataherald Makes Natural Language to SQL Easy "How Dataherald Makes Natural Language to SQL Easy" is already in English.

Part : Field Notes From Shipping Real Code With Claude

Part : Nice - my AI startup school talk is now up!

Part : Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again

Part : Automated 73% of his remote job using basic automation tools, told his manager everything, and got a promotion

Part : Building Effective AI Agents

Part : How Anthropic Teams Use Claude Code

Part : Snorting the AGI with Claude Code

Part : Nanonets-OCR-s – OCR model that transforms documents into structured markdown

Part : The Illusion of Thinking

Part : Trends – Artificial Intelligence | BOND

Part : Claude Code is My Computer | Peter Steinberger

Part : [2505.24863] AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Part : [2505.24864] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Part : My AI Skeptic Friends Are All Nuts · The Fly Blog

Part : Designing Pareto-optimal GenAI workflows with syftr

Part : "BillionMail 📧 An Open-Source MailServer, NewsLetter, Email Marketing Solution for Smarter Campaigns"

Part : Ask HN: What is the best LLM for consumer grade hardware?

Part : [2411.06037] Sufficient Context: A New Lens on Retrieval Augmented Generation Systems

Part : Show HN: Onlook – Open-source, visual-first Cursor for designers

Part : Agent Development Kit (ADK)

Part : Strands Agents

Part : Show HN: AutoThink – Boosts local LLM performance with adaptive reasoning

Part : Introduction - IntelOwl Project Documentation

Part : Show HN: My LLM CLI tool can run tools now, from Python code or plugins

Part : [2505.03335v2] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Codex’s Robot Dev Team, Grok's Fixation on South Africa, Saudi Arabia’s AI Power Play, and more...

Part : [2502.00032v1] Querying Databases with Function Calling

Part : Come Addestrare un LLM con i Tuoi Dati Personali: Guida Completa con LLaMA 3.2

Part : AI Hedge Fund

Part : Troy Hunt: Have I Been Pwned 2.0 is Now Live!

Part : A Research Preview of Codex

Part : [2505.06120] LLMs Get Lost In Multi-Turn Conversation

Part : Ollama's new engine for multimodal models

Part : Vision Now Available in Llama.cpp

Part : [2505.03335] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Requests for Startups | Y Combinator

Part : Token & Token Usage | DeepSeek API Docs

Part : Cua is Docker for Computer-Use AI Agents

Part : [2504.07139] Artificial Intelligence Index Report 2025

Part : Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

Part : DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature

Part : A foundation model to predict and capture human cognition | Nature

Part : Large language models are proficient in solving and creating emotional intelligence tests | Communications Psychology

Part : Everything About Transformers "Everything About Transformers"