# Claude Skills > ⚠️ Skills are an experimental feature under active development; skills currently reference in-flight functionality that may not be available such as the matmul operator. One of the easiest ways to get started with TT-Lang is using [Claude Code](https://claude.com/claude-code) and an existing codebase. TT-Lang provides slash commands that guide Claude through operation translation, testing, profiling, and optimization workflows. ## Example Workflow ```bash # Clone a model you want to port git clone https://github.com/karpathy/nanoGPT cd nanoGPT # Install TT-Lang slash commands (one-time setup) cd /path/to/tt-lang/claude-slash-commands ./install.sh # Open Claude Code in your project cd /path/to/nanoGPT claude # Now type slash to use skills to translate operations to TT-Lang: # /ttl-import model.py "translate the attention kernel to TT-Lang DSL" ``` ## Available Commands Run `/ttl-help` in Claude Code to see all available commands. Here is a summary: ``` /ttl-import Translate a CUDA, Triton, or PyTorch kernel to TT-Lang DSL. Analyzes the source kernel, maps GPU concepts to Tenstorrent equivalents, and iterates on testing until the translated kernel matches the original behavior. /ttl-export Export a TT-Lang operation to TT-Metal C++ code. Runs the compiler pipeline, extracts the generated C++, and beautifies it by improving variable names and removing unnecessary casts for readable, production-ready output. /ttl-optimize Profile an operation and apply performance optimizations. Identifies bottlenecks, suggests improvements like tiling, pipelining, and fusion, then validates that optimizations preserve correctness while improving throughput. /ttl-profile Run the profiler and display per-line cycle counts. Shows exactly where time is spent in the operation with annotated source, hotspot highlighting, and memory vs compute breakdown. /ttl-bug File a bug report for TT-Lang with a reproducer. /ttl-help List all available TT-Lang slash commands with descriptions and examples. ```