For AI agents: the complete documentation index is available at https://sanjaiyan-dev.github.io/urai-ecma/llms.txt, the full documentation bundle is available at https://sanjaiyan-dev.github.io/urai-ecma/llms-full.txt, and this page is available as Markdown at https://sanjaiyan-dev.github.io/urai-ecma/guide/quick-start.md.
URAI (உரை)

    Quick Start (விரைவு தொடக்கம்)

    v0.1.1 ActiveMulti-Platform Binaries< 60s SetupZero Dependencies

    Transform raw enterprise repositories into token-dense, architecturally intact Markdown prompts. Go from zero to your first synthesized LLM prompt in 4 simple steps.

    ⚡ Installation Matrix

    Select your target operating system or package ecosystem:

    🐧 macOS & Linux (Shell)
    🪟 Windows (PowerShell)
    📦 Node.js (npm / pnpm / bun)
    🦀 Rust (Cargo)
    # Downloads and installs prebuilt binary to /usr/local/bin or ~/.cargo/bin
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/sanjaiyan-dev/urai-ecma/releases/download/v0.1.1/urai-ecma-installer.sh | sh

    Verify Installation

    Run the version diagnostic in your terminal to confirm the SWC runtime is ready:

    urai-ecma --version
# Output: urai-ecma 0.1.1

    🚀 Step-by-Step Workflow

    Step 1: Scaffold Configuration

    Run urai-ecma create in the root of your project directory:

    urai-ecma create

    This generates a commented urai.config.jsonc file formatted in JSON5 (allowing comments and trailing commas):

    {
    "$schema": "https://sanjaiyan-dev.github.io/urai-ecma/json-schema/v0/config.schema.json",
    
    // Path to the project directory or single source file
    "input_project": "./src",

    // Target output Markdown file path
    "output_file": "./output.md",

    // Ollama local endpoint URL (Optional, e.g., "http://localhost:11434")
    "ollama_endpoint": "http://localhost:11434",

    // Ollama Model Name (e.g., "gemma4", "ornith")
    "ollama_modelname": "gemma4",

    // Tailwind CSS / className pruning mode: "remove" | "remove_aggr" | "summarize" | "preserve"
    // "remove": strips static class strings exceeding threshold while keeping dynamic expressions.
    // "remove_aggr": aggressively removes class strings even if below character threshold.
    // "summarize": sends class strings exceeding threshold to Ollama for 1-line style descriptions.
    // "preserve": keeps classNames untouched.
    "tailwind_mode": "remove",

    // Character length threshold for Tailwind pruning (default: 96 characters)
    "tailwind_threshold": 96,

    // Summarize function block bodies using local Ollama or fallback to JSDoc comments
    "summarize_functions": true,

    // Line count threshold to trigger function summarization (default: 5 lines)
    "summarize_functions_threshold": 5,

    // Extract and generate Express/Fastify/Next.js/NestJS API Route Table
    "generate_route_table": true,

    // Analyze React / React Native components and output detailed explanations
    "analyze_react_components": true,

    // Generate ASCII File Structure & Module Dependency Graph
    "generate_file_graph": true
}
    Precedence Hierarchy

    CLI runtime flags always override settings in urai.config.jsonc. If no config file is detected, urai-ecma uses defaults.

    Step 2: Execute Codebase Synthesis

    Run the compiler against your project directory:

    # Using the active configuration file:
urai-ecma

# Or via explicit CLI flags:
urai-ecma -i ./src -o ./prompt.md --tailwind-mode remove

    Watch the multi-threaded Rayon + SWC engine process your codebase:

    🚀 [urai-ecma] Starting AST Analysis on project: ./src
🔍 Found 48 source file(s) for analysis.
✅ [urai-ecma] Prompt successfully generated at: ./prompt.md
📊 [urai-ecma] Estimated Tokens in ./prompt.md: 24,190 tokens

============================================================
📊 TOKEN SAVINGS & OPTIMIZATION REPORT
============================================================
📁 Raw Source Code (All JS/TS):     132,450 tokens
⚡ Optimized Output (prompt.md):     24,190 tokens
------------------------------------------------------------
🎉 Reduction: -81.74% tokens saved! (Saved ~108,260 tokens)
============================================================

    Step 3: Inspect the Synthesized Prompt

    Open prompt.md. Instead of noisy utility strings and imperative loops, you will find:

    1. package.json Project Architecture: Version, dependencies, and stack overview.
    2. ASCII Project Hierarchy: Clean directory layout honoring .gitignore.
    3. Mermaid Dependency Graph: Dynamic import/export relationship maps.
    4. Backend API Route Table: Auto-discovered endpoints (Express, Fastify, Next.js App Router, NestJS).
    5. React Component Breakdowns: Typed props, hook side-effects, state variables, and handlers.
    6. AST-Pruned Source Code: Core functions converted into structural stubs with dynamic JSX intact.
    ## Backend API Route Table
| Framework | Method | Path | Handler | File Location |
| :--- | :--- | :--- | :--- | :--- |
| **Next.js** | `POST` | `/api/v1/checkout` | `POST` | `app/api/v1/checkout/route.ts:14` |
| **NestJS**  | `GET`  | `/users/:id` | `UserController::getProfile` | `src/controllers/user.ts:32` |

## React Component Breakdown: `<UserProfileCard>`
- **Props**: `user` (User), `onSelect` ((id: string) => void)
- **State**: Manages `isHovered` via setter `setIsHovered`
- **Hooks**: Uses `useState, useEffect` (Side-Effects: 1)
- **Rendered Tree**: `<div>, <Avatar>, <Badge>, <Button>`

    Step 4: Dispatch to Your Frontier LLM

    Copy prompt.md into your LLM workflow:

    💬 Claude 3.5 / 3.7 Sonnet

    Upload prompt.md directly to Projects or chat. Prevents attention dilution and cuts prefill costs by ~80%.

    🧠 ChatGPT (GPT-4o / o3)

    Fits entire enterprise systems into prompt memory with zero rate-limit (TPM) throttling.

    ⚡ Cursor & Copilot

    Reference @prompt.md in your agent context for high-precision, repo-wide refactors.

    🛠️ Tactical Command Recipes

    Copy-paste these CLI commands for common development workflows:

    Recipe A: Extreme Frontend Pruning (Design Systems & Tailwind)

    Strips all static styling while preserving dynamic clsx and conditional logic:

    urai-ecma -i ./apps/web -o ./frontend-prompt.md \
  --tailwind-mode remove_aggr \
  --tailwind-threshold 32 \
  --analyze-react-components

    Recipe B: Air-Gapped / Offline CI Pipeline (No Network, No Ollama)

    Relies strictly on AST structural stubbing and existing JSDoc comments:

    urai-ecma -i ./src -o ./ci-prompt.md \
  --summarize-functions=false \
  --tailwind-mode remove

    Recipe C: Local AI Semantic Mode (Using Ollama)

    Summarizes complex function bodies into 1-sentence explanations using local neural models:

    # Start your local Ollama daemon first:
ollama run gemma4

# Run urai-ecma targeting local Ollama:
urai-ecma -i ./src -o ./ai-prompt.md \
  --ollama-endpoint "http://localhost:11434" \
  --ollama-modelname "gemma4" \
  --summarize-functions-threshold 6

    💡 Developer Pro-Tips

    1. Zero-Config Git Awareness

    urai-ecma uses Rust's ignore::WalkBuilder under the hood. It automatically respects your .gitignore, .ignore, and hidden files, while proactively pruning heavy directories (node_modules, dist, build, .next, target).

    2. Persistent Foyer Hybrid Caching

    When Ollama summarization is enabled, results are cached in .urai-cache/ at your project root using Sha512_256 hash keys and Zstd block compression. Subsequent runs complete in milliseconds because identical functions skip LLM inference entirely.

    # To clear cached AI function summaries:
rm -rf ./src/.urai-cache