nlsc 0.2.1

Natural Language Source Compiler - Compile .nl specs to executable code

Natural Language Source (NLS)

The source code is English. The compiled artifact is Python.

NLS is a programming language where specifications are written in plain English that anyone can read—managers, auditors, domain experts—not just programmers. The nlsc compiler translates .nl files into executable Python with full type hints, validation, and documentation.

Installation

# Install from PyPI
pip install nlsc

# With tree-sitter parser (faster, better error recovery)
pip install "nlsc[treesitter]"

# For development (from source)
git clone https://github.com/Mnehmos/mnehmos.nls.lang.git
cd mnehmos.nls.lang
pip install -e ".[dev,treesitter]"

Windows File Association

Get custom icons for .nl files in Windows Explorer:

# Download and run the installer from GitHub Releases
# Or use the CLI:
nlsc assoc --user   # Current user (no admin)
nlsc assoc          # System-wide (requires admin)

Then right-click any .nl file → Open with → select the NLS launcher and check "Always use this app".

Quick Start

# Initialize a new project
nlsc init my-project
cd my-project

# Create your first .nl file
cat > src/calculator.nl << 'EOF'
@module calculator
@target python

[add]
PURPOSE: Add two numbers together
INPUTS:
  - a: number
  - b: number
RETURNS: a + b

[divide]
PURPOSE: Divide two numbers safely
INPUTS:
  - numerator: number
  - divisor: number
GUARDS:
  - divisor must not be zero -> ValueError("Cannot divide by zero")
RETURNS: numerator / divisor

@test [add] {
  add(2, 3) == 5
  add(-1, 1) == 0
}
EOF

# Compile to Python
nlsc compile src/calculator.nl

# Run the tests
nlsc test src/calculator.nl

Example

Input: math.nl

@module math
@version 1.0.0
@target python

@type Point {
  x: number
  y: number
}

[distance]
PURPOSE: Calculate distance between two points
INPUTS:
  - p1: Point
  - p2: Point
LOGIC:
  1. dx = p2.x - p1.x
  2. dy = p2.y - p1.y
  3. squared = dx * dx + dy * dy
RETURNS: sqrt(squared)
DEPENDS: [sqrt]

@test [distance] {
  distance(Point(0, 0), Point(3, 4)) == 5.0
}

Output: math.py

"""math module - Generated by nlsc"""
from dataclasses import dataclass
from math import sqrt

@dataclass
class Point:
    """Point type"""
    x: float
    y: float

def distance(p1: Point, p2: Point) -> float:
    """Calculate distance between two points"""
    dx = p2.x - p1.x
    dy = p2.y - p1.y
    squared = dx * dx + dy * dy
    return sqrt(squared)

CLI Reference

Command	Description
nlsc init <path>	Initialize new NLS project
nlsc compile <file>	Compile .nl to Python
nlsc verify <file>	Validate syntax and dependencies
nlsc test <file>	Run @test specifications
nlsc graph <file>	Generate dependency diagrams
nlsc diff <file>	Show changes since last compile
nlsc watch <dir>	Continuous compilation on file changes
nlsc atomize <file.py>	Extract ANLUs from existing Python
nlsc assoc	Install Windows file association
nlsc lsp	Start the NLS language server

Global Options

--parser {regex,treesitter}  # Parser backend (default: regex)
--version                     # Show version
--help                        # Show help

Examples

# Compile with tree-sitter parser
nlsc --parser treesitter compile src/auth.nl

# Generate Mermaid dependency diagram
nlsc graph src/order.nl --format mermaid

# Visualize dataflow for specific function
nlsc graph src/order.nl --anlu process-order --dataflow

# Watch directory and run tests on changes
nlsc watch src/ --test

# Show what changed since last compile
nlsc diff src/api.nl --full

GitHub Action

Use the NLS Compiler Action in your CI/CD pipelines for zero-config validation:

# .github/workflows/nls.yml
name: NLS Validation

on: [push, pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: Mnehmos/mnehmos.nls.lang/action@master
        with:
          verify: 'true'       # Verify all .nl files parse correctly
          compile: 'false'     # Compile .nl files to Python
          test: 'false'        # Run @test blocks
          lock-check: 'false'  # Verify lockfiles are current
          path: '.'            # Path to search for .nl files

Action Inputs

Input	Default	Description
verify	true	Verify all .nl files parse correctly
compile	false	Compile .nl files to target language
test	false	Run @test blocks from .nl files
lock-check	false	Check that lockfiles are up to date
path	.	Path to search for .nl files
python-version	3.12	Python version to use
fail-on-warning	false	Fail the action if there are warnings

Action Outputs

Output	Description
verified-files	Number of verified .nl files
compiled-files	List of compiled output files
test-results	Test pass/fail summary
warnings	Number of warnings

Language Features

ANLU Blocks (Functions)

[function-name]
PURPOSE: What this function does
INPUTS:
  - param1: type
  - param2: type, optional
GUARDS:
  - validation condition -> ErrorType("message")
LOGIC:
  1. step description -> variable
  2. another step
EDGE CASES:
  - condition -> behavior
RETURNS: expression
DEPENDS: [other-function], [another]

Type Definitions

@type Order {
  id: string, required
  items: list of OrderItem
  total: number, "Order total in cents"
  status: string
}

@type OrderItem extends BaseItem {
  quantity: number
  price: number
}

Test Specifications

@test [add] {
  add(1, 2) == 3
  add(0, 0) == 0
  add(-5, 5) == 0
}

Property-Based Testing

@property [add] {
  add(a, b) == add(b, a)           # commutativity
  add(a, 0) == a                   # identity
  forall x: number -> add(x, -x) == 0
}

Type Invariants

@type Account {
  balance: number
  owner: string
}

@invariant Account {
  balance >= 0
  len(owner) > 0
}

Directives

@module name           # Module name
@version 1.0.0         # Semantic version
@target python         # Target language
@imports other_module  # Import dependencies

The Philosophy

"The conversation is the programming. The .nl file is the receipt. The code is the artifact."

• .nl files — Human-readable specifications anyone can review
• .py files — Compiled artifacts (like assembly from C)
• .nl.lock files — Deterministic hashes for reproducible builds

Why NLS?

1. Readable by everyone — Non-programmers can review business logic
2. Auditable — Clear mapping from intent to implementation
3. Testable — Specifications include test cases
4. Versionable — Lock files ensure reproducibility
5. Toolable — Tree-sitter grammar enables IDE support

Project Status

Component	Status
Parser (regex)	✅ Complete
Parser (tree-sitter)	✅ Complete
Python emitter	✅ Complete
Type generation	✅ Complete
Guard validation	✅ Complete
Dataflow analysis	✅ Complete
Test runner	✅ Complete
Property-based testing	✅ Complete
Type invariants	✅ Complete
Watch mode	✅ Complete
GitHub Action	✅ Complete
PyPI distribution	✅ Complete
VS Code extension	✅ Complete
LSP server	✅ Complete
Windows installer	✅ Complete
TypeScript target	🔜 Planned

239 tests passing — Production-ready for Python target. See GitHub Issues for roadmap.

Documentation

📚 Full Documentation — Hosted on GitHub Pages

• Getting Started
• Language Specification
• CLI Reference
• Architecture

Contributing

# Clone and install
git clone https://github.com/Mnehmos/mnehmos.nls.lang.git
cd mnehmos.nls.lang
pip install -e ".[dev,treesitter]"

# Run tests
pytest tests/ -v

# Run tree-sitter grammar tests
cd tree-sitter-nl && npx tree-sitter test

License

MIT