GitHub Trending — 2026-08-29

#1

tt-a1i/archify

⭐ 28.3k

JavaScript 🛠️ Ferramentas Dev

📋 Problema que resolve:

简体中文 !Archify product preview Archify Turn a codebase or system description into a polished, interactive system map — directly in chat. Archify is a Node.js rendering and validation system for Cursor, Claude Code, Codex CLI, and OpenCode. Agents produce typed JSON IR; Archify deterministically compi...

🌟 Vantagens:

Layout judgment over generic auto-layout — the agent chooses hierarchy, spacing, routes, and emphasis; shared automatic endpoints spread deterministically instead of piling arrows on one midpoint.
Typed JSON IR — every renderer-backed mode has a schema and reproducible source.
Atomic validation before delivery — schema, layout, HTML/SVG, route, and label-to-route clearance checks must all pass before a showcase artifact replaces the last known good output.
Failures come with a repair receipt — validate --json and deliver --json return stable rule codes, the exact subject, measured evidence, and only supported repair controls instead of a Node stack or an unstructured retry guess.
Last-good live preview — an optional desktop loop watches one JSON file, refreshes only after the latest candidate passes every gate, and keeps the previous verified diagram visible when a save is incomplete or invalid.

#2

K-Dense-AI/scientific-agent-skills

⭐ 36.9k

Python 🤖 IA/ML

📋 Problema que resolve:

Scientific Agent Skills [![License: MIT]()](LICENSE.md) [![Version]()](pyproject.toml) [![Skills]()](-whats-included) [![Databases]()](-whats-included) [![Agent Skills]()](https://agentskills.io/) [![Agent Plugins]()](https://agent-plugins.org/) ![Security Scan](https://github.com/K-Dense-AI/scienti...

🌟 Vantagens:

The Model Is No Longer the Bottleneck — The case for why a repository like this one exists: frontier models now match specialized scientific software on raw capability (±0.079 ppm on NMR hydrogen shift prediction), so the limiting factor has moved to the workflow around the model — data access, code execution, verification, and auditable output.
The AI Co-Scientist Is Here. The Bottleneck Is Verification. — A 10-point checklist for evaluating a research agent, built around exposing sources, code, data provenance, and intermediate work rather than a polished final answer — the same reasoning behind the provenance and retrieval-contract requirements in skills like database-lookup and scientific-writing.
Reproduction, Not Generation, Is AI's Killer App for Science — Why re-running published analyses is the highest-value use of an agent: 78% of papers and 93% of individual analysis tasks reproduced across a 221-study benchmark, because a reproduction can be checked against known numbers while a generated claim cannot.
Introducing K-Bench 01: Nine Frontier Models, 178 Real Scientific Tasks, and a Lot of Confident Wrong Answers — Nine frontier models on 178 real user tasks, with overclaiming in 40% of runs. Useful calibration for what to check when an agent reports success, and context for the verification boundaries written into the clinical, regulatory, and research-methodology skills above.
### Security and safe deployment

#3

anthropics/claude-plugins-official

⭐ 35.1k

Python 🛠️ Ferramentas Dev

📋 Problema que resolve:

Claude Code Plugins Directory A curated directory of high-quality plugins for Claude Code. ⚠️ Important: Make sure you trust a plugin before installing, updating, or using it. Anthropic does not control what MCP servers, files, or other software are included in plugins and cannot verify that they wi...

🌟 Vantagens:

# Claude Code Plugins Directory
A curated directory of high-quality plugins for Claude Code.
> ⚠️ Important: Make sure you trust a plugin before installing, updating, or using it. Anthropic does not control what MCP servers, files, or other software are included in plugins and cannot verify that they will work as intended or that they won't change. See each plugin's homepage for more information.
/plugins - Internal plugins developed and maintained by Anthropic
/external_plugins - Third-party plugins from partners and the community

#4

bilawalsidhu/gods-eye-view

⭐ 11.4k

JavaScript 🎵 Mídia/Criativo

📋 Problema que resolve:

🌐 God's Eye View A spy-satellite simulator in your browser — then you realize the sources are public and the data is real. Live aircraft, ships, satellites, earthquakes, traffic, and public cameras, with clearly labeled modeled views where a live feed is unavailable. Hands-free voice control powered...

🌟 Vantagens:

### A spy-satellite simulator in your browser — then you realize the sources are public and the data is real.
Photorealistic 3D globe. Live aircraft, ships, satellites, earthquakes, traffic, and public cameras, with clearly labeled modeled views where a live feed is unavailable. Hands-free voice control powered by a realtime AI agent.
!Orbital HUD, a tracked live globe, FLIR terrain — then OPEN SOURCED
▶️ From the project behind the viral God's Eye View series *(formerly WorldView)* — 5M+ on YouTube
*Quick Start · First Five Minutes · Talk to It · What's Live · Under the Hood · Keys · Costs**

#5

abhigyanpatwari/GitNexus

⭐ 46.2k

TypeScript 🤖 IA/ML

📋 Problema que resolve:

GitNexus (Akon Labs) ⚠️ Important Notice: GitNexus has NO official cryptocurrency, token, or coin. Any token/coin using the GitNexus name on Pump.fun or any other platform is not affiliated with, endorsed by, or created by this project or its maintainers. Do not purchase any cryptocurrency claiming ...

🌟 Vantagens:

Tools like Cursor, Claude Code, Codex, Cline, Roo Code, and Windsurf are powerful — but they don't truly know your codebase structure. So this happens:
1. AI edits UserService.validate()
2. Doesn't know 47 functions depend on its return type
3. Breaking changes ship
Traditional Graph RAG gives the LLM raw graph edges and hopes it explores enough. GitNexus precomputes structure at index time — clustering, tracing, scoring — so tools return complete context in one call:

#6

JetBrains/go-modern-guidelines

⭐ 2.7k

Go 🛠️ Ferramentas Dev

📋 Problema que resolve:

![official JetBrains project](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub) Modern Go Guidelines This repository contains guidelines for code agents that help them write modern Go code. For example, an agent with these guidelines uses max(a, b) instead of an if-else block, slices...

🌟 Vantagens:

This repository contains guidelines for code agents that help them write modern Go code.
For example, an agent with these guidelines uses max(a, b) instead of an if-else block, slices.Contains instead of a manual loop, cmp.Or(a, b, c) instead of a chain of nil checks. It also knows about recent additions like new(42) to get a pointer to a value and errors.AsTypeT for type-safe error matching—both from Go 1.26.
The guidelines cover the most useful features from Go 1.0 through Go 1.27, including everything targeted by the modernize analyzer. An agent will:
Detect the project's Go version from go.mod
Use language features and stdlib additions available up to and including that version

#7

calesthio/OpenMontage

⭐ 53.5k

Python 🎵 Mídia/Criativo

📋 Problema que resolve:

Monty the Clapper — the official mascot of OpenMontage OpenMontage The first open-source, agentic video production system. Paste A Video  ·  Quick Start  ·  Try These Prompts  ·  Pipelines  ·  How It Works  ·  Sponsors  ·  Providers  ·...

🌟 Vantagens:

Most AI video tools give you a single clip from a prompt. OpenMontage gives you an end-to-end production pipeline — the same structured process a real production team follows, automated by your AI agent.
Most "free AI video" stacks quietly mean "animate still images." OpenMontage can do that too, but it can also build a finished video from real footage pulled from free/open sources, ranked semantically, edited intentionally, and rendered as a proper timeline.
Edit your own talking-head footage. Generate a fully animated explainer from scratch. Cut a 2-hour podcast into a dozen social clips. Translate and dub your content into 10 languages. Build a cinematic brand teaser from stock footage and AI-generated scenes. If a production team can make it, OpenMontage can orchestrate it.
10+ production pipelines — explainers, talking heads, screen demos, cinematic trailers, animations, podcasts, localization, documentary montages, character animation, and more
100+ production tools — spanning video generation, image creation, text-to-speech, music, audio mixing, subtitles, enhancement, and analysis

#8

abi/screenshot-to-code

⭐ 75.7k

Python 🎨 Frontend/UI

📋 Problema que resolve:

screenshot-to-code Convert screenshots, mockups, Figma designs, and screen recordings into clean, functional code using AI. The easiest way to try this is using the official, hosted product at screenshottocode.com → https://github.com/user-attachments/assets/ec08a5e6-9606-41c5-b03a-1bf47dfeba75 Supp...

🌟 Vantagens:

Convert screenshots, mockups, Figma designs, and screen recordings into clean, functional code using AI. The easiest way to try this is using
the official, hosted product at screenshottocode.com →
Gemini 3 Flash Preview and Gemini 3.1 Pro Preview - the best models
Claude Opus 4.6, Claude Opus 4.8
z-image-turbo (using Replicate) for image generation

#9

cursor/plugins

⭐ 6.0k

TypeScript 🛠️ Ferramentas Dev

📋 Problema que resolve:

Cursor plugins Official Cursor plugins for popular developer tools, frameworks, and SaaS products. Each plugin is a standalone directory at the repository root with its own .cursor-plugin/plugin.json manifest. Plugins name Plugin Author Category description (from marketplace) :-------:-------:------...

🌟 Vantagens:

Official Cursor plugins for popular developer tools, frameworks, and SaaS products. Each plugin is a standalone directory at the repository root with its own .cursor-plugin/plugin.json manifest.
| name | Plugin | Author | Category | description (from marketplace) |
|:-------|:-------|:-------|:---------|:-------------------------------------|
| teaching | Teaching | Cursor | Utilities | Skill mapping, practice plans, and learning retrospectives. |
| continual-learning | Continual Learning | Cursor | Developer Tools | Incremental transcript-driven memory updates for AGENTS.md using high-signal bullet points only. |

#10

freestylefly/awesome-gpt-image-2

⭐ 24.5k

JavaScript 🎵 Mídia/Criativo

📋 Problema que resolve:

Prompt as Code GPT-Image2 Industrial Prompt Engine & Template Library, 500+ Reverse-Engineered Cases, 20+ Industrial Templates English 简体中文日本語 🌐 Visual Website Use the live site at gpt-image2.canghe.ai to browse the gallery as a product experience: open large previews, copy full prompts, filter by ...

🌟 Vantagens:

Prompt as Code | GPT-Image2 Industrial Prompt Engine & Template Library, 500+ Reverse-Engineered Cases, 20+ Industrial Templates
Use the live site at gpt-image2.canghe.ai to browse the gallery as a product experience: open large previews, copy full prompts, filter by style or scenario, test generation after Google sign-in, and jump back to the source case on GitHub.
Join the GPT-Image2 discussion group to exchange prompts, workflows, and creative ideas with other users. Visit the community page to join.
Follow the WeChat official account 苍何 or scan the QR code below to receive project updates, new cases, and practical tutorials.
> Want to appear here? Support the project through GitHub Sponsors, or search 苍何 on WeChat and send your product name plus a short sponsorship note.

#11

tailscale/tailcat

⭐ 2.9k

Go ☸️ Infra/DevOps

📋 Problema que resolve:

"Tailscale without Tailscale, by Tailscale" Tailcat Tailcat is a remix of Tailscale open source pieces to act like netcat, but over Tailscale's data plane, without Tailscale's control plane. Tailscale's data plane (magicsock, internally) gives you point-to-point WireGuard®-encrypted tunnels between ...

🌟 Vantagens:

"Tailscale without Tailscale, by Tailscale"
Tailcat is a remix of Tailscale open source pieces to act like
netcat, but over Tailscale's data plane,
without Tailscale's control plane. Tailscale's data plane (magicsock,
internally) gives you point-to-point WireGuard®-encrypted tunnels between two

#12

NationalSecurityAgency/ghidra

⭐ 73.4k

Java 🔒 Segurança

📋 Problema que resolve:

Ghidra Software Reverse Engineering Framework Ghidra is a software reverse engineering (SRE) framework created and maintained by the [National Security Agency][nsa] Research Directorate. This framework includes a suite of full-featured, high-end software analysis tools that enable users to analyze c...

🌟 Vantagens:

# Ghidra Software Reverse Engineering Framework
Ghidra is a software reverse engineering (SRE) framework created and maintained by the
full-featured, high-end software analysis tools that enable users to analyze compiled code on a
variety of platforms including Windows, macOS, and Linux. Capabilities include disassembly,
assembly, decompilation, graphing, and scripting, along with hundreds of other features. Ghidra

#13

swoole/typephp

⭐ 844

PHP 🛠️ Ferramentas Dev

📋 Problema que resolve:

English 简体中文 TypePHP A native AOT compiler for PHP Compile PHP source code into native machine code ahead of time — producing native executables, PHP extensions, and shared libraries — while keeping the PHP syntax you already know. ![Linux x64](https://github.com/swoole/typephp/actions/workflows/lin...

🌟 Vantagens:

TypePHP is an Ahead-Of-Time (AOT) compiler that translates PHP source code into
C++ and then into native machine code. Unlike a bytecode cache or a VM, it does
not interpret opcodes at runtime: it generates optimized native binaries that
run directly on the CPU.
It keeps familiar PHP syntax and adds compile-time type information, so the

#14

marin-community/marin

⭐ 2.9k

Python 🤖 IA/ML

📋 Problema que resolve:

Marin "I am not afraid of storms, for I am learning how to sail my ship." – Louisa May Alcott Marin is a research program, software platform, and community for the research and development of foundation models. Marin's concern is training large language models. This includes data curation, transform...

🌟 Vantagens:

> "*I am not afraid of storms, for I am learning how to sail my ship.*"
Marin is a research program, software platform, and community for the research and development of foundation models.
Marin's concern is training large language models. This includes data curation, transformation, filtering, tokenization, pretraining, posttraining, and evaluation. Beyond the artifacts, software, and infrastructure, behind these models, Marin is committed to openly sharing *all* of the process knowledge required to build these models.
Marin's core value is open development. We document our processes, experiments, and decisions as they happen. Every step, from raw data to the final model, is recorded. Failed experiments are part of that record.
Marin has also been used for building audio-text models, DNA, and protein models. We encourage this work through the use of Marin as a library, in marin/experiments.

#15

tashfeenahmed/freellmapi

⭐ 21.7k

TypeScript 🤖 IA/ML

📋 Problema que resolve:

FreeLLMAPI 7.4 billion tokens per month. Aggregate free tiers from dozens of providers, plus custom OpenAI-compatible chat, embedding, image, and audio endpoints, behind a single /v1 API. A router picks the best available model for each request, falls over to the next provider when one is rate-limit...

🌟 Vantagens:

Every serious AI lab now offers a free tier, a few million tokens a month, a few thousand requests a day. On its own each tier is a toy. Stacked together, they add up to roughly 7.4 billion tokens per month of working inference capacity, across 474 model families / 635 provider endpoints from small-and-fast to reasonably capable.
The problem is that stacking them by hand is painful: thirty-four different SDKs, thirty-four different rate limits, thirty-four places a request can fail. FreeLLMAPI collapses that into one OpenAI-compatible endpoint. Point any OpenAI client library at your local server, and it routes transparently across whichever providers you've added keys for.
And the free-tier landscape shifts weekly: providers launch models, retire them, and change quotas without notice. FreeLLMAPI tracks all of that for you. The router pulls a signed model catalog from freellmapi.co on its own, so your install keeps up without a git pull. See Premium (live catalog) for how fast it keeps up.
!The free tier, stacked — ~7.4B tokens of free inference per month across 34 providers
!Feature overview

#16

ChromeDevTools/chrome-devtools-mcp

⭐ 50.0k

TypeScript 🛠️ Ferramentas Dev

📋 Problema que resolve:

Chrome DevTools for agents [![npm chrome-devtools-mcp package]()](https://npmjs.org/package/chrome-devtools-mcp) Chrome DevTools for agents (chrome-devtools-mcp) lets your coding agent (such as Antigravity, Claude, Cursor or Copilot) control and inspect a live Chrome browser. It acts as a Model-Cont...

🌟 Vantagens:

Get performance insights: Uses Chrome
DevTools to record
traces and extract actionable performance insights.
Advanced browser debugging: Analyze network requests, take screenshots and
check browser console messages (with source-mapped stack traces).

#17

rohitg00/ai-engineering-from-scratch

⭐ 50.7k

Python 📚 Educação

📋 Problema que resolve:

Türkçe Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the translations branch. From the creator of Agent Memory - 1 Persistent memory ⭐ which naturally works with any agents or chat assistants

🌟 Vantagens:

THE INDUSTRY SIGNAL
FOUNDATIONAL PAPERS COVERED
> *"The hottest new programming language is English."*
> — Andrej Karpathy (tweet)
> *"Software engineering is being remade in front of our eyes."*

#18

DietrichGebert/ponytail

⭐ 115.6k

JavaScript 🛠️ Ferramentas Dev

📋 Problema que resolve:

54% less code (up to 94%) · 20% cheaper · 27% faster · 100% safe Measured on real Claude Code sessions editing a real open-source repo (FastAPI + React), against the same agent with no skill. 54% is the mean across 12 feature tasks (Haiku 4.5, n=4); it reaches 94% where an agent...

🌟 Vantagens:

He says nothing. He writes one line. It works.
~54% less code (up to 94%) · ~20% cheaper · ~27% faster · 100% safe
Measured on real Claude Code sessions editing a real open-source repo (FastAPI + React), against the same agent with no skill. ~54% is the mean across 12 feature tasks (Haiku 4.5, n=4); it reaches 94% where an agent over-builds (a date picker) and is near zero where the code is already minimal. ponytail keeps every safety guard while a bare "write one-liners" prompt drops one. (The earlier single-shot benchmark reported 80-94% as a flat figure; against a fair agentic baseline that is the per-task ceiling, not the average.)
You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one.
Ponytail puts him inside your AI agent.

#19

google/googletest

⭐ 39.2k

C++ 🛠️ Ferramentas Dev

📋 Problema que resolve:

GoogleTest Announcements Documentation Updates Our documentation is now live on GitHub Pages at https://google.github.io/googletest/. We recommend browsing the documentation on GitHub Pages rather than directly in the repository. Release 1.18.0 Release 1.18.0 is now available

🌟 Vantagens:

xUnit test framework: \
Googletest is based on the xUnit
testing framework, a popular architecture for unit testing.
Test discovery: \
Googletest automatically discovers and runs your tests, eliminating the need

#20

livekit/agents

⭐ 13.4k

Python 🤖 IA/ML

📋 Problema que resolve:

![PyPI - Version]() ![PyPI Downloads]() [![Slack community]()](https://livekit.io/join-slack) [![Twitter Follow]()](https://twitter.com/livekit) [![Ask DeepWiki for understanding the codebase]()]() [![License]()](https://github.com/livekit/livekit/blob/master/LICENSE) Looking for the JS/TS library? ...

🌟 Vantagens:

The Agent Framework is designed for building realtime, programmable participants
that run on servers. Use it to create conversational, multi-modal voice
agents that can see, hear, and understand.
Flexible integrations: A comprehensive ecosystem to mix and match the right STT, LLM, TTS, and Realtime API to suit your use case.
Integrated job scheduling: Built-in task scheduling and distribution with dispatch APIs to connect end users to agents.

🔥 GitHub Trending — 2026-08-29