How does the skill handle API errors and rate limits?

It includes a 'safeLLMCall' utility that manages retries and implements exponential backoff specifically for 429 rate-limit errors.

Can I use this for complex reasoning tasks?

Absolutely. It includes Chain-of-Thought and Few-Shot prompting templates designed to improve the accuracy of model reasoning for complex queries.

Which LLM providers are supported by this skill?

The skill provides implementation patterns for major providers including OpenAI and Anthropic, covering both standard and streaming message completions.

Does this skill include RAG (Retrieval-Augmented Generation) logic?

Yes, it includes specialized patterns for document chunking, embedding generation, and querying vector databases like Supabase with pgvector.

LLM Application Development

Name: LLM Application Development
Author: julianromli

byjulianromli

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101

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Data Science & ML

Builds robust AI-powered features using standardized patterns for prompt engineering, RAG workflows, and multi-provider LLM integrations.

This skill provides a comprehensive toolkit for developers building Large Language Model applications, offering battle-tested patterns for prompt engineering (including structured, few-shot, and Chain-of-Thought techniques), seamless integration with OpenAI and Anthropic SDKs, and end-to-end RAG (Retrieval-Augmented Generation) implementations. It simplifies the complexities of document chunking, vector storage, and streaming responses while ensuring production readiness through built-in error handling and exponential backoff strategies for rate-limited APIs.

Key Features

01Standardized SDK integrations for OpenAI and Anthropic providers

02Complete RAG pipeline logic with document chunking and vector search

03101 GitHub stars

04Optimized streaming response patterns for low-latency user experiences

05Advanced prompt engineering patterns including Few-shot and Chain-of-Thought

06Resilient error handling with configurable retries and exponential backoff

Use Cases

01Implementing automated text classification and sentiment analysis workflows

02Building context-aware AI chatbots using proprietary data sources via RAG

03Developing complex AI agents requiring multi-step reasoning and structured output

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add julianromli/opencode-template llm-application-dev

For use in Claude.ai and ChatGPT

Download Skill