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.
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:
echo 'def main(): print("Mojo ready")' > /tmp/_verify.mojo && mojo /tmp/_verify.mojo
Running Mojo Code
Quick tests (write to temp file):
cat > /tmp/test.mojo << 'EOF'
def main():
print("hello")
EOF
mojo /tmp/test.mojo
File execution (JIT compile + run, ~1.4s overhead):
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):
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Pretrained models generate outdated Mojo.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:
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:
# 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.