Github Top Repositories

🔍 Deep-diving into mukul975/Anthropic-Cybersecurity-Skills — fresh off the trending list. 🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills 📝 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0 ────────────────────────────── The Anthropic Cybersecurity Skills GitHub repository offers a comprehensive library of 817 production-grade cybersecurity skills across 29 security domains. This library is designed to be used by AI agents, providing them with the security skills of a senior analyst. The skills are mapped to six industry frameworks, including MITRE ATT&CK, NIST CSF 2.0, and MITRE F3, making it a unified cross-framework coverage. To get started, users can clone the repository or use npx skills add mukul975/Anthropic-Cybersecurity-Skills for immediate integration with compatible platforms like Claude Code, GitHub Copilot, and OpenAI Codex CLI. This repository is ideal for security professionals, developers, and enterprise teams looking to enhance their AI agents' security capabilities. It's an AI-native knowledge base built from the ground up for the agentskills.io standard, providing structured decision-making workflows for senior analysts. In a nutshell, Anthropic Cybersecurity Skills is the key to unlocking your AI agent's full security potential — giving it the skills to outsmart threats and outmaneuver attackers. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 ZhuLinsen/daily_stock_analysis just hit the trending charts — here's why it matters. 🔗 https://github.com/ZhuLinsen/daily_stock_analysis 📝 LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs. ────────────────────────────── Daily Stock Analysis is a stock intelligent analysis system that provides daily automatic analysis and pushes a "decision dashboard" to various platforms. The system uses AI models to analyze stocks and provides features such as multi-market data aggregation, web/desktop workspace, and agent strategy inquiry. The system supports various AI models and data sources, including Anspire, AIHubMix, and SerpAPI. It also provides a web interface for configuration management, task monitoring, and manual analysis. To use the system, you can fork the repository and configure the secrets, or use the client configuration tutorial for local deployment. The system also provides a docker deployment option. Key features include: * AI decision reports * Multi-market data aggregation * Web/desktop workspace * Agent strategy inquiry * Automatic push Audience: This project is suitable for individual investors and financial professionals who want to use AI technology to analyze stocks. Technical highlights: * Python 3.10+ * GitHub Actions * Docker One-liner takeaway: Automate your stock analysis with Daily Stock Analysis, a powerful AI-driven system that provides daily insights and decision-making support. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 calesthio/OpenMontage caught my eye on GitHub Trending today. 🔗 https://github.com/calesthio/OpenMontage 📝 World's first open-source, agentic video production system. 12 pipelines, 52 tools, 500+ agent skills. Turn your AI coding assistant into a full video production studio. ────────────────────────────── Introducing OpenMontage, the first open-source, agentic video production system. This innovative platform allows you to create stunning videos by simply describing what you want in plain language. Your agent will handle research, scripting, asset generation, editing, and final composition. With OpenMontage, you can produce high-quality videos, including animated explainers, documentary montages, and even cinematic trailers. The system supports various tools and providers, such as Remotion, FFmpeg, and Pexels, to name a few. To get started, simply git clone the repository, install the prerequisites, and run the setup script. Then, open the project in your AI coding assistant and describe your video idea. The agent will take care of the rest, from scriptwriting to rendering the final video. Key features include: * Support for various video styles and formats * Automatic scriptwriting, narration, and subtitles * Integration with multiple tools and providers * Free and open-source, with optional paid API keys for additional features The target audience for OpenMontage includes: * Content creators looking for an efficient and cost-effective video production solution * Developers interested in exploring the potential of agentic video production * Anyone looking to create high-quality videos without extensive video production experience In summary, OpenMontage is a game-changing video production platform that empowers you to create stunning videos with ease. With its innovative agentic approach, extensive feature set, and open-source nature, it's an exciting tool for anyone looking to push the boundaries of video creation. Unlock the full potential of video production with OpenMontage — and discover a whole new world of creative possibilities! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🔍 Deep-diving into mattpocock/skills — fresh off the trending list. 🔗 https://github.com/mattpocock/skills 📝 Skills for Real Engineers. Straight from my .claude directory. ────────────────────────────── The mattpocock/skills GitHub repository contains a collection of skills designed to enhance the productivity and effectiveness of coding agents. These skills are the result of decades of engineering experience and are meant to be small, adaptable, and composable, working with any model. The skills are categorized into engineering, productivity, and misc groups, each containing user-invoked and model-invoked skills. Key features of the repository include: - grill-me and grill-with-docs skills for aligning with the agent and building a shared language - tdd skill for test-driven development with a red-green-refactor loop - diagnosing-bugs skill for disciplined diagnosis of hard bugs and performance regressions - improve-codebase-architecture skill for rescuing a codebase that has become a ball of mud These skills can be used by developers to improve their workflow, reduce misalignment with the agent, and produce better code. To get started, users can run the skills.sh installer and follow the quickstart guide. The repository is designed for real engineers, not just "vibe coding," and is meant to be used in conjunction with coding agents like Claude Code. Overall, the mattpocock/skills repository offers a powerful set of tools for enhancing coding productivity and effectiveness. Take your coding to the next level with these skills - your agent will thank you! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 lyogavin/airllm landed on trending. Worth a proper look. 🔗 https://github.com/lyogavin/airllm 📝 AirLLM 70B inference with single 4GB GPU ────────────────────────────── The airllm repository on GitHub is a game-changer for large language models. Its primary purpose is to optimize inference memory usage, allowing massive models like 70B and 405B Llama3.1 to run on a single 4GB GPU without quantization, distillation, and pruning. Key features include model compression for up to 3x inference speedup, support for various models like Llama2, ChatGLM, QWen, and more. The repository provides an easy-to-use interface, with a pip install airllm command to get started. Technical highlights comprise block-wise quantization-based model compression, which reduces disk loading bottlenecks while maintaining accuracy. The AutoModel class automatically detects model types, eliminating the need to specify model classes. The target audience includes data scientists, researchers, and developers working with large language models, particularly those looking to optimize model performance on limited hardware. To get started, simply install the airllm package and initialize the model with the desired configuration. With airllm, you can run massive language models on modest hardware - a total paradigm shift in NLP: Run massive models, not massive GPUs. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 JCodesMore/ai-website-cloner-template just hit the trending charts — here's why it matters. 🔗 https://github.com/JCodesMore/ai-website-cloner-template 📝 Clone any website with one command using AI coding agents ────────────────────────────── Ai Website Cloner Template is a reusable template for reverse-engineering any website into a clean, modern Next.js codebase using AI coding agents. The recommended AI agent for this template is Claude Code with Opus 4.7, but it supports a variety of AI coding agents. To use the template, you can create your own repository from it, open it on your computer, install dependencies with npm install, start your AI agent, and run the /clone-website skill with the target URL. You can then customize the cloned website as needed. The template has several key features, including: - Reconstructing every section of the target website - Extracting design tokens and assets - Writing component specs - Dispatching parallel builders to reconstruct the website From a technical standpoint, the template uses: - Next.js 16 with App Router, React 19, and TypeScript strict - shadcn/ui with Radix primitives and Tailwind CSS v4 - Tailwind CSS v4 with oklch design tokens - Lucide React for default icons The template is suitable for platform migration, lost source code, and learning use cases. However, it is not intended for phishing, impersonation, or passing off someone's design as your own. In summary: Ai Website Cloner Template is a powerful tool for reverse-engineering websites, and with the right AI agent, you can clone any website into a modern Next.js codebase - the future of web development just got a whole lot easier. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 firecrawl/firecrawl caught my eye on GitHub Trending today. 🔗 https://github.com/firecrawl/firecrawl 📝 The API to search, scrape, and interact with the web at scale. 🔥 ────────────────────────────── Firecrawl is an API designed to search, scrape, and interact with the web at scale. It offers industry-leading reliability, covering 96% of the web, including JS-heavy pages, and provides blazingly fast results with a P95 latency of 3.4s. Key features include: - Search: Search the web and get full page content from results - Scrape: Convert any URL to markdown, HTML, screenshots, or structured JSON - Interact: Scrape a page, then interact with it using AI prompts or code - Agent: Automated data gathering, just describe what you need - Crawl: Scrape all URLs of a website with a single request - Map: Discover all URLs on a website instantly - Batch Scrape: Scrape thousands of URLs asynchronously The API is open source and available as a hosted service, making it easy to integrate into your project. To get started, simply sign up at firecrawl.dev to get your API key and try the playground to test it out. Here's a simple example of how to use the Search endpoint in Python:

from firecrawl import Firecrawl

app = Firecrawl(api_key="fc-YOUR_API_KEY")

search_result = app.search("firecrawl", limit=5)

And here's an example of how to use the Scrape endpoint in Node.js:

import { Firecrawl } from 'firecrawl';

const app = new Firecrawl({apiKey: "fc-YOUR_API_KEY"});

app.scrape('firecrawl.dev')

Firecrawl is perfect for developers, data scientists, and AI researchers who need to extract data from the web. With its easy-to-use API and fast results, it's the perfect tool for anyone looking to power their AI agent or build a web scraping application. One-liner takeaway: Firecrawl is the ultimate web scraping API that helps you search, scrape, and interact with the web at scale, making it easy to build powerful AI applications and extract valuable data from the web. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🌟 ZhuLinsen/daily_stock_analysis caught my eye on GitHub Trending today. 🔗 https://github.com/ZhuLinsen/daily_stock_analysis 📝 LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs. ────────────────────────────── Daily Stock Analysis is a powerful AI-driven stock analysis system designed to provide users with a comprehensive and intelligent analysis of their selected stocks. The system offers key features such as AI decision reports, multi-market data aggregation, web/desktop workspaces, and automatic push notifications to various platforms like WeChat, Telegram, and email. To get started, users can choose from two methods: GitHub Actions or local/client deployment. The GitHub Actions method allows for zero-cost deployment in just 5 minutes, while the local/client deployment method requires installing dependencies and configuring environment variables. Technically, the system utilizes a range of AI models, including Anspire, AIHubMix, and Gemini, and supports various data sources like TickFlow, AkShare, and Tushare. The system is designed for individual investors and researchers looking to gain a deeper understanding of the stock market and make informed investment decisions. In a nutshell, Daily Stock Analysis is an intelligent and user-friendly system that empowers users to take control of their investments with data-driven insights. Takeaway: With Daily Stock Analysis, you can unlock the full potential of AI-driven stock analysis and make informed investment decisions with ease. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 DeusData/codebase-memory-mcp landed on trending. Worth a proper look. 🔗 https://github.com/DeusData/codebase-memory-mcp 📝 High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies. ────────────────────────────── Introducing codebase-memory-mcp, the fastest and most efficient code intelligence engine for AI coding agents. This cutting-edge tool full-indexes an average repository in milliseconds and the Linux kernel in just 3 minutes. It answers structural queries in under 1ms, making it a game-changer for developers. Key features include high-quality parsing through tree-sitter AST analysis across 158 languages, Hybrid LSP semantic type resolution for 11 languages, and a persistent knowledge graph of functions, classes, call chains, and more. The tool is plug and play across 11 coding agents, with zero dependencies and a single static binary for macOS, Linux, and Windows. Technical highlights include RAM-first pipeline with LZ4 compression, in-memory SQLite, and fused Aho-Corasick pattern matching. The tool also features built-in graph visualization and infrastructure-as-code indexing for Dockerfiles, Kubernetes manifests, and Kustomize overlays. Usage is straightforward, with a one-line install command and auto-sync background watcher for detecting file changes. The tool is designed for developers and researchers looking to supercharge their coding workflow with AI-powered code intelligence. Takeaway: With codebase-memory-mcp, you can index your codebase in minutes and query it in milliseconds – it's a total game-changer for coding productivity. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 bytedance/deer-flow landed on trending. Worth a proper look. 🔗 https://github.com/bytedance/deer-flow 📝 An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours. ────────────────────────────── DeerFlow is an open-source super agent harness that enables efficient research and exploration by orchestrating sub-agents, memory, and sandboxes with extensible skills. It's a ground-up rewrite of the original Deep Research framework, now available in version 2.0. Key features include skills and tools like Claude Code integration, sub-agents, sandbox and file system, context engineering, and long-term memory. To get started, you can clone the repository and run the setup wizard with make setup, then choose your deployment option: make dev for local development, make docker-start for Docker development, or make up for a long-running server. DeerFlow is ideal for developers, researchers, and power users looking for a flexible and customizable platform. One-liner takeaway: DeerFlow is a powerful, open-source super agent harness that streamlines research and exploration with its unique combination of sub-agents, memory, and sandboxes. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe