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llm-patterns

Couple LLM-driven logic with deterministic, testable TypeScript plumbing.

Updated Jan 30, 2026
Search Tags:#typescript#testing#ai-patterns#prompt-patterns#prompt-management#llm-patterns
One-click install
npx skills add https://github.com/aberrantCode/WinScanOllamaVision --skill llm-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-patterns
Source: https://github.com/aberrantCode/WinScanOllamaVision/tree/main/.claude/skills/llm-patterns
Command: npx skills add https://github.com/aberrantCode/WinScanOllamaVision --skill llm-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps AI teams design and reuse reliable, pattern-based approaches for building AI-first applications, reducing brittle prompts and ad-hoc plumbing by separating concerns between prompt logic and execution.

Core Features & Use Cases

  • LLM-driven logic patterns: verbal reasoning, classification, and decision-making guided by structured prompts and templates.
  • Typed LLM client and testing: clear interfaces, validation of LLM outputs, and deterministic unit/integration tests.
  • Prompt management and anti-patterns: versioned prompts, safe guardrails, and auditing of prompt changes across projects.
  • Use Case: Build an AI assistant that classifies user inquiries, routes them to the right service, and logs reasoning for debugging and compliance.

Quick Start

Load a base prompt kit and apply a language-specific pattern to your first use case without writing boilerplate code.

Frequently Asked Questions about llm-patterns

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

FAQPage Schema
How do I build robust LLM workflows with TypeScript that avoid brittle prompts?▼

You can build robust LLM workflows by separating prompt logic from execution using typed LLM clients, structured prompt templates, and deterministic unit tests to ensure reliable AI application behavior.

What is the best way to test LLM outputs in an AI application?▼

The best way to test LLM outputs is coupling typed LLM clients with deterministic unit and integration testing workflows, validating structured outputs against expected patterns for auditable AI logic.

How do I manage and version prompt templates across different projects?▼

Manage prompt templates by applying versioned prompt patterns and guardrails, enabling safe auditing of prompt changes and preventing ad-hoc plumbing across your AI projects.

Do I need a specialized AI platform to use pattern-driven prompt workflows?▼

No, you do not need a specialized AI platform. Pattern-driven prompt workflows rely on standard tooling like Node.js, TypeScript, and prompt templates without requiring specialized infrastructure.

Can I use typed LLM clients to route user inquiries and log reasoning?▼

Yes, typed LLM clients can classify user inquiries, route them to appropriate services, and log verbal reasoning for debugging and compliance using structured prompt patterns.

Why should I separate prompt logic from execution plumbing in LLM applications?▼

Separating prompt logic from execution plumbing prevents brittle prompts and ad-hoc code, enabling pattern-based AI application development with safe guardrails and auditable workflows.

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