coding-mojo: Скилл для разработки и запуска Mojo кода

--- name: coding-mojo description: Develop and run Mojo code in Claude.ai containers. Handles installation, compilation, and execution. Use when writing Mojo code, benchmarking Mojo vs Python, or when user mentions Mojo, Modular, or MAX. Routes to Modular's official skills (mojo-syntax, mojo-python-interop, mojo-gpu-fundamentals) for language-specific correction layers. metadata: version: 0.2.1 --- # Mojo Development in Claude.ai Containers Mojo is a systems programming language from Modular that combines Python-like syntax with C-level performance. This skill handles container setup and execution. For **language syntax and semantics**, defer to Modular's official skills at `github.com/modular/skills` — they are authoritative correction layers for pretrained knowledge. ## Installation Install once per session (~20s via uv, ~500MB). Skip if already installed. ```bash if mojo --version 2>/dev/null; then echo "Mojo already installed" else # Compiler binary without ML extras (~350MB saved) uv pip install --system --break-system-packages modular --no-deps 2>&1 | tail -5 # Entry points + base deps (numpy, pyyaml, rich) uv pip install --system --break-system-packages mojo max 2>&1 | tail -5 mojo --version fi ``` Verify: ```bash echo 'def main(): print("Mojo ready")' > /tmp/_verify.mojo && mojo /tmp/_verify.mojo ``` ## Running Mojo Code **Quick tests** (write to temp file): ```bash cat > /tmp/test.mojo << 'EOF' def main(): print("hello") EOF mojo /tmp/test.mojo ``` **File execution** (JIT compile + run, ~1.4s overhead): ```bash cat > /home/claude/example.mojo << 'EOF' def main(): print("Hello from Mojo") EOF mojo /home/claude/example.mojo ``` **Build binary** (for benchmarking — ~6s cold compile, but binary runs at native speed): ```bash mojo build /home/claude/example.mojo -o /home/claude/example /home/claude/example ``` Use `mojo build` for benchmarks — `mojo` (JIT) includes ~1.4s compilation overhead per run. There is no `mojo -e` flag; always write to a file. ## Critical Syntax Corrections (v26.2) Pretrained models generate outdated Mojo. These corrections are current as of Mojo 26.2: | Wrong (pretrained) | Correct (26.2) | Notes | |---|---|---| | `fn main():` | `def main():` | `fn` is deprecated; `def` is the only function keyword | | `let x = 5` | `var x = 5` | `let` removed; `var` for all bindings | | `inout self` | `mut self` / `out self` | `mut` for mutation, `out` for `__init__` | | `@parameter for` | `comptime for` | Compile-time loops | | `List[Int](1, 2, 3)` | `[1, 2, 3]` | Collection literals | | `from math import sqrt` | `from std.math import sqrt` | `std.` prefix required for **all** stdlib modules | | `from time import X` | `from std.time import X` | Includes `perf_counter_ns`, `sleep`, etc. | | `__str__` / `Stringable` | `write_to` / `Writable` | String conversion protocol | | `String(self.x)` for int→str | `String(self.x)` | This one is actually correct, but `str()` is not | | `list.append(item)` | `list.append(item^)` | Non-copyable types require `^` transfer operator | | `var x: Int = perf_counter_ns()` | `var x: UInt = perf_counter_ns()` | Time functions return `UInt`, not `Int` | | Implicit copy of `List[T]` | `.copy()` or `^` transfer | `List` is not implicitly copyable; use explicit copy or move | ## Companion Skills (Modular Official) These skills from `github.com/modular/skills` provide deep syntax correction layers. If they are installed in the user's skill set, read them before writing Mojo code: - **mojo-syntax** — Comprehensive syntax corrections, type system, ownership model. **Always use when writing any Mojo code.** - **mojo-python-interop** — Calling Python from Mojo, type conversion, extension modules. Use when mixing Mojo and Python. - **mojo-gpu-fundamentals** — GPU programming (no CUDA syntax — Mojo has its own model). Reference only in Claude.ai containers (no GPU available). - **new-modular-project** — Project scaffolding with Pixi or uv. Use when starting a new Mojo/MAX project locally. If companion skills are not installed, the correction table above covers the most common pretrained errors. For deeper work, fetch the skill content directly: ```bash curl -sL -H "Authorization: token $GH_TOKEN" -H "Accept: application/vnd.github.v3.raw" "https://api.github.com/repos/modular/skills/contents/mojo-syntax/SKILL.md?ref=main" ``` ## Container Constraints - **No GPU**: Claude.ai containers are CPU-only. GPU skills are reference material for generating code the user will run locally. - **Session-ephemeral**: Mojo installation doesn't persist across conversations. Reinstall each session. - **Build artifacts**: Store in `/home/claude/`. Copy final outputs to `/mnt/user-data/outputs/`. - **Timeout**: Long compilations or benchmarks may hit the ~200s bash timeout. Break work into smaller units. ## Benchmarking Pattern Compare Mojo vs Python on the same algorithm: ```bash # Python baseline python3 -c " import time def fib(n): a, b = 0, 1 for _ in range(n): a, b = b, a + b return a # Warmup + timed runs fib(90) times = [] for _ in range(100): start = time.perf_counter() fib(90) times.append((time.perf_counter() - start) * 1e6) import statistics print(f'Python: median={statistics.median(times):.1f} µs, min={min(times):.1f} µs') " # Mojo version cat > /home/claude/fib.mojo << 'EOF' from std.time import perf_counter_ns def fib(n: Int) -> Int: var a = 0 var b = 1 for _ in range(n): var tmp = a a = b b = tmp + b return a def main(): # Warmup _ = fib(90) # Timed runs var total_ns: UInt = 0 var min_ns: UInt = 999999999 for _ in range(100): var start = perf_counter_ns() _ = fib(90) var elapsed = perf_counter_ns() - start total_ns += elapsed if elapsed < min_ns: min_ns = elapsed print("Mojo: mean =", total_ns // 100, "ns, min =", min_ns, "ns") EOF mojo build /home/claude/fib.mojo -o /home/claude/fib /home/claude/fib ``` Expected: Mojo is ~50x faster than CPython on tight numeric loops. SIMD and parallelism widen the gap further but require mojo-syntax and mojo-gpu-fundamentals skills for correct usage.