timber
CommunityCompile ML models to native C.
Authorkossisoroyce
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the process of compiling trained classical machine learning models into highly optimized, dependency-free C99 inference artifacts, enabling microsecond-latency serving and deployment on edge devices.
Core Features & Use Cases
- Model Compilation: Compiles XGBoost, LightGBM, scikit-learn, CatBoost, and ONNX models into C99, LLVM IR, or WebAssembly.
- Zero Runtime Dependencies: Emits pure C99 code with no external libraries required for inference.
- Use Case: Deploy a fraud detection model trained in XGBoost to a resource-constrained embedded system or a high-throughput microservice without the overhead of Python runtimes.
Quick Start
Use the timber skill to compile the attached model file 'model.json' into a C99 artifact.
Dependency Matrix
Required Modules
clicknumpyrichrequeststomli
Components
scriptsreferencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: timber Download link: https://github.com/kossisoroyce/timber/archive/main.zip#timber Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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