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workers-multi-lang

@secondsky/claude-skills
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Multi-language Workers development with Rust, Python, and WebAssembly. Use when building Workers in languages other than JavaScript/TypeScript, or when integrating WASM modules for performance-critical code.

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SKILL.md

name workers-multi-lang
description Multi-language Workers development with Rust, Python, and WebAssembly. Use when building Workers in languages other than JavaScript/TypeScript, or when integrating WASM modules for performance-critical code.
version 1.0.0

Multi-Language Workers Development

Build Cloudflare Workers using Rust, Python, or WebAssembly for performance-critical operations.

Language Comparison

Feature JavaScript/TS Rust Python
Startup Fast Fastest (WASM) Moderate
CPU Perf Good Excellent Good
Memory Higher Lower Higher
Bundle Size Smaller Medium Larger
Type Safety Optional (TS) Strict Optional
Best For General apps CPU-intensive Data/ML

Quick Decision

Need maximum performance? → Rust/WASM
Heavy computation (crypto, image processing)? → Rust/WASM
Data processing, ML inference? → Python
General web apps? → JavaScript/TypeScript

Top 10 Multi-Lang Errors

Error Language Cause Solution
WebAssembly.instantiate() failed Rust Invalid WASM Check wasm-pack build output
Module parse failed: Unexpected token Rust ESM/CJS mismatch Use --target bundler
Cannot find module Python Missing dep Add to pyproject.toml
Out of memory All Large WASM Enable streaming instantiation
Exceeded CPU time limit All Long computation Chunk processing
wasm-bindgen version mismatch Rust Dep conflict Align versions in Cargo.toml
RuntimeError: unreachable Rust Panic in WASM Add proper error handling
TypeError: not a function Rust Missing export Add #[wasm_bindgen] attribute
Python worker startup timeout Python Slow init Minimize imports
SharedArrayBuffer not supported All Security Add COOP/COEP headers

Rust Quick Start

# Install tools
cargo install wasm-pack

# Create project
cargo new --lib my-worker
cd my-worker

# Add to Cargo.toml
cat >> Cargo.toml << 'EOF'
[lib]
crate-type = ["cdylib"]

[dependencies]
wasm-bindgen = "0.2"
worker = "0.3"
console_error_panic_hook = "0.1"

[profile.release]
opt-level = "s"
lto = true
EOF
// src/lib.rs
use worker::*;

#[event(fetch)]
async fn fetch(req: Request, env: Env, _ctx: Context) -> Result<Response> {
    console_error_panic_hook::set_once();

    Router::new()
        .get("/", |_, _| Response::ok("Hello from Rust!"))
        .get("/compute", |_, _| {
            // CPU-intensive computation
            let result = heavy_computation();
            Response::ok(format!("Result: {}", result))
        })
        .run(req, env)
        .await
}

fn heavy_computation() -> u64 {
    (1..1_000_000).filter(|n| is_prime(*n)).count() as u64
}

fn is_prime(n: u64) -> bool {
    if n < 2 { return false; }
    (2..=(n as f64).sqrt() as u64).all(|i| n % i != 0)
}

Python Quick Start (Workers for Platforms)

# pyproject.toml
[project]
name = "my-worker"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = []

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
# src/entry.py
from js import Response, Headers

async def on_fetch(request, env):
    url = request.url

    if "/compute" in url:
        result = heavy_computation()
        return Response.new(f"Result: {result}")

    return Response.new("Hello from Python!")

def heavy_computation():
    """CPU-intensive computation"""
    return sum(1 for n in range(2, 100000) if is_prime(n))

def is_prime(n):
    if n < 2:
        return False
    return all(n % i != 0 for i in range(2, int(n**0.5) + 1))

WASM Module Integration

// Load and use WASM module in TypeScript Worker
import wasmModule from './pkg/my_lib_bg.wasm';
import { init, process_data } from './pkg/my_lib';

let wasmInstance: WebAssembly.Instance;

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Initialize WASM once
    if (!wasmInstance) {
      wasmInstance = await WebAssembly.instantiate(wasmModule);
      init();
    }

    // Use WASM function
    const result = process_data(inputData);

    return Response.json({ result });
  },
};

When to Load References

Reference Load When
references/rust-workers.md Building Workers with Rust/WASM
references/python-workers.md Using Python on Workers for Platforms
references/wasm-integration.md Integrating WASM modules in any Worker

Performance Tips

  1. WASM Initialization: Cache instance, use streaming
  2. Memory: Use typed arrays for data transfer
  3. Bundle Size: Enable LTO, strip debug info
  4. Cold Starts: Keep WASM modules small
  5. Data Transfer: Minimize JS/WASM boundary crossings

See Also

  • workers-performance - General optimization techniques
  • workers-testing - Testing multi-language Workers
  • cloudflare-worker-base - Basic Workers setup