How does it handle API rate limits and errors?

It features a safe LLM call wrapper that implements exponential backoff for rate limits and configurable retry logic for transient failures.

What prompt engineering techniques are included?

The skill supports structured system prompts, few-shot examples, and Chain-of-Thought (CoT) reasoning templates to improve model output quality.

Can I use this for Retrieval-Augmented Generation (RAG)?

Absolutely. The skill provides patterns for document chunking, embedding storage, and vector database querying to build complete RAG pipelines.

Does this skill support multiple AI providers?

Yes, it includes standardized implementation patterns for both OpenAI and Anthropic SDKs, as well as generic streaming response handling.

LLM Application Development

Name: LLM Application Development
Author: plurigrid

byplurigrid

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Data Science & ML

Streamlines the creation of AI-powered applications through advanced prompt engineering, RAG orchestration, and multi-model API integration.

About

This skill provides a comprehensive framework for building production-grade LLM applications by offering standardized patterns for structured prompts, few-shot learning, and Chain-of-Thought reasoning. It bridges the gap between raw model access and robust software engineering, including built-in support for Retrieval-Augmented Generation (RAG) pipelines, intelligent document chunking, and resilient error handling with exponential backoff. Additionally, it incorporates unique scientific skill interleaving and categorical structures to ensure compositional coherence in complex AI workflows.

Key Features

Advanced Prompt Engineering (Few-Shot, CoT, Structured)
2 GitHub stars
Multi-Model API Integration for OpenAI and Anthropic
End-to-end RAG Pipeline and Vector Storage Patterns
Resilient Error Handling with Exponential Backoff
Scientific Skill Interleaving with Graph Theory Support

Use Cases

Building context-aware customer support chatbots using RAG
Orchestrating complex multi-step reasoning tasks for data analysis
Implementing robust AI backend services with streaming and fallback logic

About

Key Features

Advanced Prompt Engineering (Few-Shot, CoT, Structured)
2 GitHub stars
Multi-Model API Integration for OpenAI and Anthropic
End-to-end RAG Pipeline and Vector Storage Patterns
Resilient Error Handling with Exponential Backoff
Scientific Skill Interleaving with Graph Theory Support

Use Cases

Building context-aware customer support chatbots using RAG
Orchestrating complex multi-step reasoning tasks for data analysis
Implementing robust AI backend services with streaming and fallback logic