skills.rest
kossisoroyce
kossisoroyceCOMMUNITY
Explore all skills by this author →
Website𝕏@kossisoroyceGitHub

timber

Compile classical machine learning models into native C99 inference artifacts.

688|23|Updated Feb 27, 2026
Search Tags:#embedded#compiler#inference#ml#c99#xgboost
One-click install
npx skills add https://github.com/kossisoroyce/timber --skill timber
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: timber
Source: https://github.com/kossisoroyce/timber/tree/main
Command: npx skills add https://github.com/kossisoroyce/timber --skill timber

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires click, numpy, rich, requests, tomli, and includes scripts (resource) and references (resource) and assets (resource) components.

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.

Frequently Asked Questions about timber

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compile XGBoost models to C99 for embedded deployment?▼

You can compile XGBoost models to C99 by using automated compilation passes that emit dependency-free native code. This process outputs pure C99 inference artifacts optimized for microsecond-latency serving and edge devices without requiring Python runtimes.

Can I convert scikit-learn and LightGBM models into native C code?▼

Yes, you can convert scikit-learn and LightGBM models into native C code. The compilation supports ONNX and CatBoost formats as well, transforming trained classical machine learning models into optimized C99, LLVM IR, or WebAssembly artifacts.

Do I need gcc or clang to compile ML models into C99 inference artifacts?▼

Yes, you need gcc or clang to compile ML models into C99 inference artifacts. A C compiler is required to build the generated pure C99 code into a functional, zero-runtime-dependency binary for high-performance serving.

What is the best way to deploy fraud detection models on resource-constrained edge devices?▼

The best way to deploy fraud detection models on edge devices is compiling them into pure C99 code. This eliminates external library overhead, enabling microsecond-latency inference directly on resource-constrained embedded systems.

Does compiling ML models to C99 support pipeline fusion and dead leaf elimination?▼

Yes, compiling ML models to C99 supports pipeline fusion and dead leaf elimination. These optimization passes are applied during the compilation process to streamline the generated native code and maximize inference performance.

Related Skills

Explore More Software Engineering Skills→
EchoMuraEchoMura

timber-compiler

Convert XGBoost, LightGBM, scikit-learn, CatBoost, and ONNX models into C99 inference binaries.

Community
Advanced
gmh5225gmh5225
868

binary-lifting

Translates machine code into LLVM IR for analysis and de-ossification.

Community
Advanced
LeandroBenjaminLLeandroBenjaminL

ml-modeling

Train machine learning models with validation and explainability using scikit-learn, lightgbm, xgboost, and shap.

Community
Advanced
plurigridplurigrid
60

borkdude

Guide ClojureScript runtime selection across scripting, browser, and Node contexts.

Official
Advanced