skills.rest
ProgMastermind
ProgMastermindCOMMUNITY
Explore all skills by this author →
Website𝕏@PrakashCollymo1GitHub

atom-patterns

Provide coding patterns and architecture guidance for optimizing the ATOM LLM inference engine.

Updated Jul 15, 2026
Search Tags:#optimization#LLM#coding patterns#inference engine#ATOM
One-click install
npx skills add https://github.com/ProgMastermind/ATOM --skill atom-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: atom-patterns
Source: https://github.com/ProgMastermind/ATOM/tree/main/.claude/skills/atom-patterns
Command: npx skills add https://github.com/ProgMastermind/ATOM --skill atom-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides coding patterns and architecture guidance for optimizing the ATOM LLM inference engine, enhancing performance and efficiency.

Core Features & Use Cases

  • Code Architecture: Offers a detailed directory structure and module descriptions for the ATOM engine.
  • Model Implementation: Provides a template for adding new models and reusing existing ones.
  • Workflows: Describes workflows for adding models, fixing bugs, and debugging.
  • Testing: Outlines testing patterns and the use of pytest for unit testing.
  • Environment Variables: Lists and explains environment variables for configuring the ATOM engine.
  • CI/CD: Details the CI/CD setup for accuracy testing and benchmarking.

Quick Start

Use the atom-patterns skill to review the directory structure of the ATOM engine and understand the architecture of model loading and execution.

Frequently Asked Questions about atom-patterns

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

FAQPage Schema
How do I optimize the ATOM LLM inference engine for better performance?▼

To optimize the ATOM LLM inference engine, apply specific coding patterns and architecture guidance for model loading and execution to enhance overall performance and efficiency.

What is the recommended directory structure for the ATOM inference engine?▼

The recommended directory structure for the ATOM inference engine organizes modules logically to support model loading, execution, and the addition of new models, ensuring a maintainable codebase.

How do I add a new model to the ATOM inference engine?▼

You add a new model to the ATOM inference engine by following the defined workflow to implement the model template, which allows you to reuse existing architectures while maintaining structural consistency.

What testing patterns should I use for ATOM inference engine development?▼

You should use pytest for unit testing when developing the ATOM inference engine, following the outlined testing patterns to ensure model implementation correctness and execution reliability.

Do I need Python knowledge to implement ATOM coding patterns?▼

Yes, you need Python knowledge and an understanding of ATOM's architecture to implement these coding patterns effectively within your LLM applications.

How do I configure environment variables for the ATOM engine?▼

You configure the ATOM engine by setting specific environment variables that control model execution behavior, which are listed and explained within the provided architecture guidance.

Related Skills

Explore More Software Engineering Skills→
quodsolerquodsoler
304

ue-project-context

Create an Unreal Engine project context document capturing engine version, modules, plugins, and conventions.

Community
Advanced
JeffallanJeffallan
10.9k

test-master

Automate unit, integration, E2E, performance, and security testing across CI/CD pipelines.

Community
Advanced
agentuityagentuity
1

agentuity-cli-cloud-sandbox-snapshot-get

Retrieve structured JSON details for a specific cloud sandbox snapshot.

Official
Intermediate
tdiminotdimino
41

llama-cpp

Run GGUF model inference with llama.cpp and apply LoRA adapters.

Community
Advanced