bck-nd-hlpr 1.0.4

The Backend Helper - Lightweight Architecture CLI

🛠️ Backend Helper (bck-nd-hlpr)

Lightweight Architecture CLI - Reverse-engineer any codebase into Mermaid diagrams with AI-powered insights.

Backend Helper is a lightweight CLI tool that automatically detects backend architectures and generates MERMAID diagrams. Built for CI/CD pipelines, code reviews, and rapid onboarding.

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⚡ Key Features

• 🔍 Auto-Detection: Identifies Flask, FastAPI, Django, Next.js, Express.js, NestJS, Gin, Actix-web, and more
• 🏭 Architecture Recognition: Detects MVC, Microservices, Layered Architecture patterns
• Smart Diagrams: Different visualizations for Controllers, Models, Services, Routes
• 🤖 BYO-Key AI Analysis: Directly integrate with OpenAI, Anthropic, Gemini, or local Ollama using API keys. No middleware needed!
• 🌐 Cloud AI Fallback: Support for 9 AI personalities via n8n webhooks if no keys are provided.
• 🛡️ Dependency-Free Core: No PyTorch, No Transformers. Installs in <3 seconds
• 🪟 OS-Safe Scanning: Robust directory traversal ignoring venv, node_modules, and system restricted files
• 🎨 Visual & Mermaid: Output Unicode diagrams or copy-paste Mermaid code
• 🚀 Auto-Documentation (CI/CD): One-command setup for GitHub Actions to host living docs on GitHub Pages (init-ci)
• 🧠 AI Context Dump (bck-nd prompt): Export a single, LLM-optimized .txt file with project tree + UML + ER + core files — just copy-paste into ChatGPT/Claude for instant codebase understanding [NEW ✨]
• ⚙️ Flexible Config: Customize detection via pyproject.toml
• 🌍 Polyglot Ready: C# (.NET Core), Python (Django/FastAPI), JavaScript/TypeScript (Next.js/Express.js/Drizzle ORM), Java (Spring Boot), PHP (Laravel), Go, Rust, Docker, Terraform, Prisma schemas (schema.prisma), and SQL migrations (.sql)

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📦 Installation

# From source
cd bck-nd-hlpr
pip install .

# Development mode
pip install -e .

# Verify
bck-nd --help

# Optional: Set your preferred AI Provider key
# set OPENAI_API_KEY=sk-... (Windows)
# export OPENAI_API_KEY=sk-... (Mac/Linux)

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📚 Command Manual

🖥️ explore - Interactive TUI Mode (Explorer) 🆕

Launch a full-screen Terminal User Interface (TUI) to interactively explore your project's architecture, powered by textual.

Usage

bck-nd explore

What you get:

• Sidebar: Directory tree to navigate your codebase.
• Main View: Click on a .py file to instantly generate its ASCII diagram and Mermaid Sequence routes.
• Dynamic Analysis: Click on a folder to see the high-level architecture of that specific directory.
• Shortcuts: Press D to toggle dark/light mode, Q to quit.

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🌐 docs - Static HTML Portal Generation 🆕

Automatically generates a complete, static HTML documentation portal for your project. Perfect for CI/CD and GitHub Pages.

Usage

# Generate docs in the current directory (output folder: 'docs')
bck-nd docs . --output docs

What you get in docs/index.html:

• Infrastructure Map: Visual representation of docker-compose.yml.
• API Routes: Sequence diagrams of HTTP endpoints.
• UML Class Diagram: Auto-generated class hierarchy with associations and dependencies.
• Entity-Relationship: E-R diagrams for ORM models (Entity Framework, SQLAlchemy, Django).
• Technical Debt: Actionable table of TODOs and FIXMEs.
• Fully self-contained, using MermaidJS CDN for rendering. No heavy build tools required.

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🧠 prompt - AI Context Dump 🆕

Generates a single, LLM-optimized .txt file with XML-like tags that you can copy-paste directly into ChatGPT, Claude, or any AI to give it instant, complete understanding of your project.

No more manually explaining your codebase structure — one command, one file, instant AI context.

Usage

# Generate ai_context.txt in the current directory
bck-nd prompt .

# Custom output file
bck-nd prompt /my/project -o context.txt

# Deeper scan (default depth is 4)
bck-nd prompt . --depth 6

What the file contains

XML Tag	Contents
<project_tree>	Clean ASCII directory tree (no venv/node_modules)
<architecture_uml>	UML Class Diagram in Mermaid format
<architecture_er>	Entity-Relationship Diagram in Mermaid format
<core_files>	Content of the 3-5 most important backend files

How to use it

1. Run bck-nd prompt . in your project root
2. Open ai_context.txt
3. Select All → Copy
4. Paste into ChatGPT / Claude as the first message
5. Start asking questions about your codebase immediately!

Example output structure

<!-- bck-nd-hlpr Context Dump -->
<!-- Paste this file into ChatGPT / Claude for instant AI context -->

<project_tree>
my-project/
+-- src/
|   +-- main.py
|   +-- models.py
\-- tests/
</project_tree>

<architecture_uml>
```mermaid
classDiagram
    class User { ... }

</architecture_uml>

<architecture_er>

erDiagram
    User { int id PK }

</architecture_er>

<core_files> <file path="src/main.py">

# ... file content ...

```

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🚀 init-ci - GitHub Actions Automation 🆕

Set up "Living Documentation" in seconds. This command injects a ready-to-use GitHub Action into your repository.

Usage

bck-nd init-ci

What it does:

• Creates .github/workflows/bck-nd-docs.yml.
• Configures an automatic trigger on push to main.
• Installs bck-nd-hlpr in the CI runner.
• Generates the full HTML portal (UML, ER, Infra, Routes).
• Deploys the result automatically to GitHub Pages.

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🕵️ scan - Automatic Architecture Detection

Automatically scans your project, detects the framework and architecture, and generates intelligent diagrams.

Basic Usage

# Scan current directory (default depth: 3)
bck-nd scan .

# Scan specific directory
bck-nd scan src

# Custom depth
bck-nd scan . --depth 5

Modes

1. Full Architecture Overview (Default)

bck-nd scan .

Output:

• Framework detection (Flask, FastAPI, Django, etc.)
• Architecture type (MVC, Microservices, etc.)
• Features (Docker, Auth, Database, etc.)
• Infra Map: Docker Compose services
• API Routes: Endpoints sequence diagram
• UML & ER: Class and Entity-Relationship Mermaid diagrams
• TODOs: Technical Debt Report

2. Mermaid Export (New!)

bck-nd scan . --format mermaid

Output:

• Generates graph TD code ready to copy-paste into Notion, GitHub, or Obsidian.
• Also shows the specific visual diagram in the terminal for instant preview.
• Perfect for documentation and presentations.

3. UML Class Diagram

bck-nd scan . --uml

• Generates classDiagram code for Mermaid.js.
• Uses a unified multi-language parser combining AST (Python) and Tree-Sitter (C#, Java, JS/TS, PHP) to extract classes, methods, properties, and constructors automatically.
• Automatically infers relationships (--> Associations, ..> Dependencies) and inheritance (<|--) across all files.

3. Diagram + Local Report

bck-nd scan . --explain

Output:

• Everything from mode 1, PLUS
• Text-based component breakdown
• List of Controllers, Models, Services
• No AI required (100% offline)

5. Entity-Relationship Diagram (ER) 🆕

bck-nd scan . --er

Output:

• Generates erDiagram for Mermaid.js.
• Scans modern schema configurations, migrations, and ORMs across languages:
  • Modern Configs: Prisma Schemas (schema.prisma), Drizzle ORM schemas (.ts/.js), and raw SQL migrations (.sql)
  • Traditional ORMs: Entity Framework (C#), Spring Boot / JPA (Java), Laravel / Eloquent (PHP), SQLAlchemy / Django models (Python), and Sequelize / Mongoose (JS/TS)
• Bulletproof Mermaid Syntax: Safely handles Generics (e.g. List<T>), table brackets, and special characters.
• Detects database columns, primary keys (PK), data annotations, and auto-generates bidirectional relationships (||--o{, }o--||) with intelligent schema deduplication and merging.

6. API Route Map 🆕

bck-nd scan . --routes

Output:

• Generates sequenceDiagram for Mermaid.js.
• Scans Flask and FastAPI endpoints.
• Visualizes Client -> API interactions with methods and paths.

7. Infrastructure Diagram 🆕

bck-nd scan . --infra

Output:

• Generates graph LR for Mermaid.js.
• Scans docker-compose.yml files.
• Shows services, images, and dependencies.
• Database services (postgres, redis, mysql, mongo) displayed as cylinders.

8. Technical Debt Scanner 🆕

bck-nd scan . --todo

Output:

• Scans for TODO, FIXME, HACK, XXX, BUG comments
• Beautiful color-coded table using Rich
• Shows file, line number, type, and message
• Statistics by debt type
• Debt level assessment
• Perfect for code reviews and sprint planning

9. Security Audit 🆕

bck-nd scan . --audit

Output:

• Scans for hardcoded secrets, keys, and dangerous config
• Reports "Critical" risks like AWS Keys or Private PEMs
• Reports "High/Warning" risks like DB passwords or hardcoded IPs
• Essential for pre-commit checks

10. Dependency Heatmap 🆕

bck-nd scan . --impact

Output:

• Shows a "Heatmap" of your files based on how many other files import them.
• Helps identify "Core" modules that are risky to refactor.
• Sorts by Impact Score and assigns Risk Categories (🔥 CORE, 🟡 SHARED, 🟢 PERIPHERAL).

10.5 Route-to-DB Traceability 🆕

bck-nd scan . --trace

Output:

• Generates graph LR for Mermaid.js.
• Traces API calls starting from your routes down to your services and models.
• Parses AST (currently supports Python: FastAPI/Flask).

11. Diagram + AI Analysis

bck-nd scan . --ai

Output:

• Everything from mode 1, PLUS
• AI-powered architectural analysis
• Design pattern recommendations
• Code quality insights
• Detects API keys in your environment (OpenAI, Anthropic, Gemini, Ollama) or falls back to Webhook (n8n).

11. Force Specific AI Provider 🆕

bck-nd scan . --ai --provider openai

Output:

• Supported providers: openai, anthropic, gemini, ollama, webhook.
• Safely reports an error if the corresponding API key is missing.

12. AI Only (No Diagram)

bck-nd scan . --no-graph --ai

Output:

• Only AI analysis (no Mermaid diagram)
• Faster for text-only reports

AI Personalities

# Professional analysis
bck-nd scan . --ai --style pro

# Security-focused review
bck-nd scan . --ai --style hacker

# Critical code review (like Gordon Ramsay)
bck-nd scan . --ai --style ramsay

# Simple explanations
bck-nd scan . --ai --style eli5

# Available styles (Mostly optimized for the Webhook / n8n Mode):
# pro, hacker, soviet, eli5, ramsay, jarvis, corporate, medieval, doom

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📐 flow - Manual Diagram Generation

Create custom architecture diagrams from string descriptions.

Usage

bck-nd flow "Client -> API -> Database"

bck-nd flow "Client -> LoadBalancer -> [API_v1, API_v2] ; API_v1 -> Redis"

bck-nd flow "User -> Auth [Service] -> JWT [Token] -> API"

Syntax

• A -> B - Creates connection from A to B
• [X, Y, Z] - Multiple nodes in same position
• ; - New row
• [DB], [SQL], [DATA] - Rendered as database cylinders
• [Service], [DIR] - Rendered as soft boxes
• [?], [IF] - Rendered as diamonds

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🎯 Usage Examples

Example 1: Quick Project Analysis

cd my-backend-project
bck-nd scan .

What you get:

🔍 Analizando arquitectura de '.'...
💻 Framework detectado: FastAPI
🏭 Arquitectura: REST API (Route-based)
✨ Características: Docker, SQLAlchemy ORM, Authentication

📝 FastAPI application using REST API (Route-based) with Docker, SQLAlchemy ORM, Authentication.

📊 DIAGRAMA DE ARQUITECTURA:
[ASCII diagram showing Routes -> Services -> Models -> Database]

Example 2: Deep Analysis with AI

bck-nd scan . --ai --style pro --depth 5

What you get:

• Complete architecture detection
• Full project diagram
• AI analysis including:
  • Design pattern recommendations
  • Security considerations
  • Performance optimization suggestions
  • Code quality assessment

Example 3: Text-Only Report

bck-nd scan src --explain --no-graph

What you get:

• Framework/architecture detection
• Component list without diagram
• Perfect for CI/CD logs

Example 4: Compare Two Approaches

# Old monolith
bck-nd scan ./legacy --ai --style ramsay

# New microservices
bck-nd scan ./new-arch --ai --style pro

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🔧 Architecture Detection

Backend Helper automatically detects:

Frameworks

Language	Frameworks
Python	Flask, FastAPI, Django (Specialized ER/UML), Quart
JavaScript/TypeScript	Next.js (Filesystem Routes & React UML), Express.js (Specialized ER/UML), Fastify, Koa, NestJS (Route Detection)
Java	Spring Boot (Specialized ER/UML), Maven, Gradle
PHP	Laravel (Specialized ER/UML)
C# / .NET	.NET Core, Entity Framework (Specialized ER/UML)
Go	Gin, Fiber
Rust	Actix-web, Rocket

Architecture Patterns

• Microservices Architecture - Multiple services in docker-compose
• MVC + Services (Layered) - Controllers, Models, Services folders
• MVC Pattern - Controllers + Models
• REST API (Route-based) - Routes + Models
• Containerized Application - Docker detected
• Monolithic Application - Fallback

Features Detection

• Docker / Docker Compose
• Databases (SQL, SQLite)
• ORM (SQLAlchemy, Django ORM)
• Authentication (JWT, OAuth)
• API Documentation (Swagger/OpenAPI)
• CI/CD (GitHub Actions, GitLab CI)
• Unit Tests
• Security: Auto-redaction of secrets in output (Sanitizer) 🆕

Configuration

You can override architecture detection by adding this to pyproject.toml:

[tool.bck-nd]
controllers = ["handlers", "views"]
models = ["entities", "schemas"]
services = ["logic", "usecases"]

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💾 Output Persistence (New!)

You can now save any report or diagram to a file using -o / --output. The tool automatically strips ANSI color codes for clean text files.

# Save ASCII diagram
bck-nd scan . -o architecture.txt

# Save Technical Debt Report (Clean text)
bck-nd scan . --todo -o report.txt

# Save Mermaid diagram
bck-nd scan . --er --format mermaid -o db.mmd

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🧪 AI Providers Setup (BYO-Key)

Backend Helper supports direct integration with modern AI models using your own keys. It automatically loads .env files from your current directory! By default, it checks environment variables in this order: OPENAI -> ANTHROPIC -> GOOGLE -> GROQ -> DEEPSEEK -> OPENROUTER -> OLLAMA -> WEBHOOK

Option 1: .env File (Recommended)

Simply create a .env file in your project root:

OPENAI_API_KEY=sk-...
# or
GROQ_API_KEY=gsk_...
# or
DEEPSEEK_API_KEY=sk-...

And run:

bck-nd scan . --ai

Option 2: Environment Variables

Alternatively, export them directly:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GOOGLE_API_KEY="AIzaSy..."
export GROQ_API_KEY="gsk_..."
export DEEPSEEK_API_KEY="sk-..."
export OPENROUTER_API_KEY="sk-or-v1-..."

Option 3: Ollama (Local AI)

No API key required. Make sure Ollama is running on http://localhost:11434.

# Optionally customize the host
export OLLAMA_HOST="http://localhost:11434"
bck-nd scan . --ai --provider ollama

Option 5: n8n Webhook Fallback

If no keys are found, it falls back to the legacy webhook approach.

1. Install n8n

npm install -g n8n

2. Start n8n

n8n start

Access: http://localhost:5678

3. Create Workflow

1. Add Webhook trigger
   • Method: POST
   • Path: explain
2. Add AI node (OpenAI/Gemini)
   • System: {{ $json.prompt }}
   • Input: {{ $json.text }}
3. Add Respond to Webhook
   • JSON: { "text": "{{ $json.output }}" }
4. Activate workflow

4. Custom Webhook (Optional)

# Windows
set BCK_ND_WEBHOOK_URL=https://your-server.com/webhook/explain

# Linux/Mac
export BCK_ND_WEBHOOK_URL=https://your-server.com/webhook/explain

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🤖 MCP Integration (Claude Desktop / Cursor)

Backend Helper includes a server compatible with the Model Context Protocol (MCP). This allows any compatible AI client (like Claude Desktop or Cursor) to interact directly with your codebase using our local reverse engineering and diagramming tools without needing to send all your code to the cloud or consume valuable context tokens by transferring full files.

The AI will call local tools on demand to analyze the architecture, generate diagrams, search for technical debt, or audit security.

How to Configure

1. Claude Desktop

Add the following configuration block to your claude_desktop_config.json file:

• Windows Path: %APPDATA%\Claude\claude_desktop_config.json
• Mac/Linux Path: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "backend-helper": {
      "command": "python",
      "args": [
        "c:/bck-nd-hlpr/mcp_server.py"
      ],
      "env": {
        "OPENAI_API_KEY": "your-optional-api-key",
        "ANTHROPIC_API_KEY": "your-optional-api-key"
      }
    }
  }
}

2. Cursor

1. Go to Cursor Settings > Features > MCP.
2. Click on + Add New MCP Server.
3. Configure the following parameters:
   • Name: backend-helper
   • Type: stdio
   • Command: python c:/bck-nd-hlpr/mcp_server.py
4. Save and click on Refresh. Done! You will instantly have 11 architecture tools available for your AI.

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Comparison: Different Commands

Command	Architecture Detection	Diagram	Text Report	AI Analysis	AI Context File
bck-nd scan .	✅	✅ (Full Arch)	❌	❌	❌
bck-nd scan . --explain	✅	✅	✅	❌	❌
bck-nd scan . --ai	✅	✅	❌	✅	❌
bck-nd scan . --explain --ai	✅	✅	✅	✅	❌
bck-nd scan . --no-graph --ai	✅	❌	❌	✅	❌
bck-nd scan . --uml	✅	✅ (UML Class)	❌	❌	❌
bck-nd scan . --er	✅	✅ (ER DB)	❌	❌	❌
bck-nd scan . --routes	✅	✅ (API Seq)	❌	❌	❌
bck-nd scan . --infra	✅	✅ (Docker LR)	❌	❌	❌
bck-nd scan . --todo	✅	❌	✅ (Debt)	❌	❌
bck-nd scan . --audit	✅	❌	✅ (Sec. Risks)	❌	❌
bck-nd scan . --impact	✅	❌	✅ (Impact Heatmap)	❌	❌
bck-nd scan . --trace	✅	✅ (Trace LR)	❌	❌	❌
bck-nd prompt .	✅	✅ (Mermaid)	❌	❌	✅ (XML)
bck-nd flow "A -> B"	❌	✅	❌	❌	❌
bck-nd explore	✅	✅	✅	❌	❌
bck-nd docs .	✅	✅ (All HTML)	✅ (HTML Portal)	❌	❌
bck-nd chat .	✅	✅ (Loaded)	❌	✅ (Interactive)	❌
bck-nd init-ci	✅	✅	✅	❌	❌

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🎭 AI Personalities Guide

Style	Description	Use Case
pro	Senior Software Architect - Technical, formal	Production documentation
hacker	Security Expert - Focuses on vulnerabilities	Security audits
soviet	Soviet Engineer - Efficiency-focused	Performance reviews
eli5	Kindergarten Teacher - Simple explanations	Onboarding juniors
ramsay	Gordon Ramsay - Brutally critical	Code reviews
jarvis	Tony Stark's AI - Elegant, helpful	Executive presentations
corporate	Manager - Buzzword-heavy	Stakeholder reports
medieval	Ancient Wizard - Metaphorical	Creative documentation
doom	Doom Slayer - Bugs are demons	Bug hunting

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🐛 Troubleshooting

"No se encontraron archivos"

Solution:

# Increase depth
bck-nd scan . --depth 5

# Or scan specific directory
bck-nd scan src --depth 3

"Error conexión: ..."

Cause: n8n not running Solution:

# Terminal 1
n8n start

# Terminal 2
bck-nd scan . --ai

"Framework detectado: Unknown"

Cause: Framework not yet supported or non-standard structure Solution: Use bck-nd flow for manual diagrams

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📊 Supported File Types

Type	Detection Method	Output Shape
Controllers	*controller.py, *ctrl.py	Box → API
Models	*model.py, *entity.py, *schema.py	Box → Database (Cylinder)
Services	*service.py, *svc.py	Box → Business Logic
Routes	*route.py, *router.py	Box → Endpoints
Middleware	*middleware.py	Box → Request Pipeline
Database Files	.sql, .db, .sqlite	Cylinder → Data Storage
Docker	Dockerfile, docker-compose.yml	Soft Box
Infrastructure	.tf (Terraform)	Box → Infrastructure

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🚀 What's Different from ASCII Architect?

Feature	ASCII Architect	Backend Helper
Auto-Detection	❌	✅ Flask, FastAPI, Django, etc.
Architecture Patterns	❌	✅ MVC, Microservices, etc.
Component Classification	❌	✅ Controllers, Models, Services
Neural Engine (GPT-2)	✅	❌ Removed for speed
Installation Size	~500MB	<50MB
Installation Time	~2 min	<3 sec
Cloud AI	✅	✅ 9 personalities

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📝 Real-World Usage

CI/CD Integration

Option A: Automatic Setup (Recommended)

# Run this once locally to inject the workflow
bck-nd init-ci
git add . && git commit -m "ci: add auto-documentation" && git push origin main

Option B: Manual YAML

# .github/workflows/arch-analysis.yml
- name: Analyze Architecture
  run: |
    pip install bck-nd-hlpr
    bck-nd scan . --explain --no-graph > architecture.txt

Code Review Automation

# Before PR approval
bck-nd scan . --ai --style pro > review.md

Documentation Generation

Generate architecture docs

bck-nd scan . --explain > docs/ARCHITECTURE.md bck-nd scan . --ai --style pro > docs/AI_ANALYSIS.md

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## 📚 Documentation

- [IA-context.md](IA-context.md) - Development rules & architecture
- [ROADMAP.txt](ROADMAP.txt) - Feature roadmap
- [MANIFEST.in](MANIFEST.in) - Package configuration

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## 💡 Philosophy

> **"Less guessing, more coding."**

Backend Helper is designed for **speed**, **intelligence**, and **actionable insights**. No bloated dependencies, no waiting for model downloads. Just instant architectural understanding.

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## 🤝 Contributing

Issues and PRs welcome! See [IA-context.md](IA-context.md) for development guidelines.

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## 📄 License

MIT License - See LICENSE file for details

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**Built with ❤️ for developers who value clarity and speed.**