10 viral AI repositories on GitHub this week:

1. Understand-Anything

Turns any codebase into an interactive knowledge graph.

→ Visualize structure, dependencies, and logic like a map → Search and ask questions about the code → Works with Claude Code, Cursor, Copilot, and Gemini CL

https://github.com/Lum1104/Understand-Anything…

2. Codegraph

Indexes code upfront so agents don’t search files on every query.

→ -57% tokens and -62% tool calls → 100% local

https://github.com/colbymchenry/codegraph…

3. MoneyPrinterTurbo

Generates short videos using just keywords.

→ Creates script, materials, subtitles, and music → Vertical 9:16 and horizontal 16:9

https://github.com/harry0703/MoneyPrinterTurbo…

4. ECC

Enhances multi-agent environments like Claude Code, Cursor, and Codex.

→ Skills, memory, and security in one package → 63 agents and 249 skills

https://github.com/affaan-m/ECC

5. Taste-skill

Takes AI out of generic UI designs.

→ Applies pro-level layout, typography, and white space → Works with Claude, Cursor, and Codex

https://github.com/Leonxlnx/taste-skill…

6. knowledge-work-plugins

11 official Anthropic plugins to turn Claude into an area-specific expert.

→ Sales, support, marketing, legal, and finance → Designed for Claude Cowork, also work in Claude Code

https://github.com/anthropics/knowledge-work-plugins…

7. Anthropic-Cybersecurity-Skills

754 cybersecurity skills for your agents.

→ Compatible with 5 frameworks, including MITRE ATT&CK → Note: Has “Anthropic” in the name but it’s community-made, not official

https://github.com/mukul975/Anthropic-Cybersecurity-Skills…

8. academic-research-skills

Covers the full research workflow: papers, review, and correction.

→ AI handles the tedious stuff (like verifying citations), you keep the judgment → “AI as copilot” design

https://github.com/Imbad0202/academic-research-skills…

9. pi

Full kit for developing AI agents.

→ Coding CLI, unified API for multiple LLMs, execution infra, and terminal UI → Integrates with Slack and secures the supply chain

https://github.com/earendil-works/pi…

10.skills (official from Anthropic)

Official collection of Agent Skills examples. → Write the procedure in SKILL.md and reuse it in Claude Code, http://Claude.ai, and API → Template for creating your own skills

https://github.com/anthropics/skills…

Week’s summary: code graphs + skills for agents.

AI is starting to read code like a map and switch skills based on what it needs.

If you work with agents, this is what’s coming.

Save this list

Egonex-AI/Understand-Anything

Source: https://github.com/Egonex-AI/Understand-Anything

Understand Anything

Turn any codebase, knowledge base, or docs into an interactive knowledge graph you can explore, search, and ask questions about.
Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.

English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Español | Türkçe | Русский

💬 Join the Discord community →
Ask questions, share what you've built, get help from the community.

You just joined a new team. The codebase is 200,000 lines of code. Where do you even start?

Understand Anything is a Claude Code Plugin that analyzes your project with a multi-agent pipeline, builds a knowledge graph of every file, function, class, and dependency, then gives you an interactive dashboard to explore it all visually. Stop reading code blind. Start seeing the big picture.

The goal isn’t a graph that wows you with how complex your codebase is — it’s a graph that quietly teaches you how every piece fits together.

✨ Features

Want to skip the reading? Try the live demo in our homepage — a fully interactive dashboard you can pan, zoom, search, and explore right in your browser.

Explore the structural graph

Navigate your codebase as an interactive knowledge graph — every file, function, and class is a node you can click, search, and explore. Select any node to see plain-English summaries, relationships, and guided tours.

Understand business logic

Switch to the domain view and see how your code maps to real business processes — domains, flows, and steps laid out as a horizontal graph.

Analyze knowledge bases

Point /understand-knowledge at a Karpathy-pattern LLM wiki and get a force-directed knowledge graph with community clustering. The deterministic parser extracts wikilinks and categories from index.md, then LLM agents discover implicit relationships, extract entities, and surface claims — turning your wiki into a navigable graph of interconnected ideas.

🚀 Quick Start

1. Install the plugin

/plugin marketplace add Lum1104/Understand-Anything
/plugin install understand-anything

2. Analyze your codebase

/understand

A multi-agent pipeline scans your project, extracts every file, function, class, and dependency, then builds a knowledge graph saved to .understand-anything/knowledge-graph.json.

Localized output: Use --language to generate content in your preferred language:

# Generate Chinese content (知识图节点描述和 Dashboard UI)
/understand --language zh

# Supported languages: en (default), zh, zh-TW, ja, ko, ru

On the first run in a project — when you don’t pass --language and no language is stored yet — /understand detects the language you’re conversing in. If it isn’t English, it asks you to confirm (or override) before generating; English conversations are unaffected. Your choice is saved to .understand-anything/config.json and reused on every later run.

The --language parameter affects:

• Node summaries and descriptions in the knowledge graph
• Dashboard UI labels, buttons, and tooltips
• Guided tour explanations

3. Explore the dashboard

/understand-dashboard

An interactive web dashboard opens with your codebase visualized as a graph — color-coded by architectural layer, searchable, and clickable. Select any node to see its code, relationships, and a plain-English explanation.

4. Keep learning

# Ask anything about the codebase
/understand-chat How does the payment flow work?

# Analyze impact of your current changes
/understand-diff

# Deep-dive into a specific file or function
/understand-explain src/auth/login.ts

# Generate an onboarding guide for new team members
/understand-onboard

# Extract business domain knowledge (domains, flows, steps)
/understand-domain

# Analyze a Karpathy-pattern LLM wiki knowledge base
/understand-knowledge ~/path/to/wiki

# Re-run anytime — incremental by default (only re-analyzes changed files)
/understand

# Auto-update on every commit via a post-commit hook
/understand --auto-update

# Scope to a subdirectory (for huge monorepos)
/understand src/frontend

🌐 Multi-Platform Installation

Understand-Anything works across multiple AI coding platforms.

Claude Code (Native)

/plugin marketplace add Lum1104/Understand-Anything
/plugin install understand-anything

One-line install (Codex / OpenCode / OpenClaw / Antigravity / Gemini CLI / Pi Agent / Vibe CLI / VS Code Copilot / Hermes / Cline / KIMI CLI / Trae)

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/Lum1104/Understand-Anything/main/install.sh | bash
# or skip the prompt by passing the platform:
curl -fsSL https://raw.githubusercontent.com/Lum1104/Understand-Anything/main/install.sh | bash -s codex

Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/Lum1104/Understand-Anything/main/install.ps1 | iex

The installer clones the repo to ~/.understand-anything/repo and creates the right symlinks for the chosen platform. Restart your CLI/IDE afterwards.

• Supported <platform> values: gemini, codex, opencode, pi, openclaw, antigravity, vibe, vscode, hermes, cline, kimi, trae
• Update later: ./install.sh --update
• Uninstall: ./install.sh --uninstall <platform>

Cursor

Cursor auto-discovers the plugin via .cursor-plugin/plugin.json when this repo is cloned. No manual installation needed — just clone and open in Cursor.

If auto-discovery doesn’t pick it up, install it manually: open Cursor Settings → Plugins, paste https://github.com/Lum1104/Understand-Anything into the search field, and add it from there.

VS Code + GitHub Copilot

VS Code with GitHub Copilot (v1.108+) auto-discovers the plugin via .copilot-plugin/plugin.json when this repo is cloned. No manual installation needed — just clone and open in VS Code.

For personal skills (available across all projects), run the install.sh above with the vscode platform.

Copilot CLI

copilot plugin install Lum1104/Understand-Anything:understand-anything-plugin

Platform Compatibility

📦 Share the Graph with Your Team

The graph is just JSON — commit it once, and teammates skip the pipeline. Good for onboarding, PR reviews, and docs-as-code.

Example: GoogleCloudPlatform/microservices-demo (fork) — Go / Java / Python / Node reference with a committed graph.

What to commit: everything in .understand-anything/ except intermediate/ and diff-overlay.json (those are local scratch).

.understand-anything/intermediate/
.understand-anything/diff-overlay.json

Keep it fresh: enable /understand --auto-update — a post-commit hook incrementally patches the graph so each commit lands with a matching graph. Or re-run /understand manually before releases.

Large graphs (10 MB+): track with git-lfs.

git lfs install
git lfs track ".understand-anything/*.json"
git add .gitattributes .understand-anything/

🔧 Under the Hood

Tree-sitter + LLM hybrid

Static analysis and LLMs do what each does best:

• Tree-sitter (deterministic) — parses source into a concrete syntax tree and extracts structural facts: imports, exports, function/class definitions, call sites, inheritance. Pre-resolved into an importMap during the scan phase and passed to file-analyzers so they don’t re-derive imports from source. Same input → same output, every run. Also powers fingerprint-based change detection for incremental updates.
• LLM (semantic) — reads the parsed structure alongside the original source to produce what parsers can’t: plain-English summaries, tags, architectural layer assignments, business-domain mapping, guided tours, language concept callouts.

This split is why the graph is reproducible on the structural side (the same code always yields the same edges) while still capturing intent on the semantic side (what a file is for, not just what it imports).

Multi-Agent Pipeline

The /understand command orchestrates 5 specialized agents, and /understand-domain adds a 6th:

File analyzers run in parallel (up to 5 concurrent, 20-30 files per batch). Supports incremental updates — only re-analyzes files that changed since the last run.

🎥 Community

A community-made walkthrough by Better Stack.

Watch on YouTube →

Made a video, blog post, or tutorial? Open an issue or PR — happy to feature it here.

🤝 Contributing

Contributions are welcome! Here’s how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Run the tests (pnpm --filter @understand-anything/core test)
4. Commit your changes and open a pull request

Please open an issue first for major changes so we can discuss the approach.

Stop reading code blind. Start understanding everything.

Star History

Thanks to everyone who's used and contributed — knowing this saves people time is what made it worth building.

MIT License © Lum1104