# llm-application-dev

A skill for building LLM-powered features: prompt patterns, a RAG pipeline, API calls and retry handling.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 462 installs · ★ 1153
- Author: MoizIbnYousaf
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/MoizIbnYousaf/ai-agent-skills/tree/HEAD/skills/llm-application-dev/SKILL.md
- Updated: 2026-09-21
- HTML: https://affpapa.org/en/nevibecode/skills/llm-application-dev

## What it does

The skill hands your agent ready-made templates for code that talks to language models. Inside are structured system prompts with rules and a context slot, few-shot examples for classification, and chain-of-thought prompts. Integration patterns cover OpenAI and Anthropic clients in TypeScript, plus streaming with `stream: true`.

RAG gets its own section. The pipeline runs in steps: embed the question, search the vector database, build context, generate an answer. There is a chunking function with overlap and an example of storing embeddings in Supabase with pgvector. For failures, a wrapper retries with exponential backoff on a 429.

The skill does not create API keys. Sample code reads `OPENAI_API_KEY` and `ANTHROPIC_API_KEY` from environment variables, so nothing runs without them. Model names and limits like `max_tokens: 500` are baked into the examples — worth checking against your own budget and task instead of copying them as they are.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/llm-application-dev/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llm-application-dev/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/llm-application-dev/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/llm-application-dev/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install MoizIbnYousaf/ai-agent-skills llm-application-dev
```

2. **Manually: put the files in place**
- File: `.github/skills/llm-application-dev/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/MoizIbnYousaf/ai-agent-skills --path skills/llm-application-dev
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llm-application-dev/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/MoizIbnYousaf/ai-agent-skills/tree/HEAD/skills/llm-application-dev/SKILL.md

## When to use it
- write a system prompt with rules and a context slot
- wire a RAG pipeline from question embedding to answer
- split a document into overlapping chunks
- add retries when the API returns 429
- stream model output into a chat UI

## Example prompts
- build a RAG pipeline over our docs on Supabase with pgvector
- add exponential backoff retries to our LLM call on 429
- make the Anthropic call stream into our chat

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0d46df5b64e49108.

BEGIN THIRD-PARTY FILE 0d46df5b64e49108
````text
---
name: llm-application-dev
description: Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
source: wshobson/agents
license: MIT
version: 4.1.0
---

# LLM Application Development

## Prompt Engineering

### Structured Prompts
```typescript
const systemPrompt = `You are a helpful assistant that answers questions about our product.

RULES:
- Only answer questions about our product
- If you don't know, say "I don't know"
- Keep responses concise (under 100 words)
- Never make up information

CONTEXT:
{context}`;

const userPrompt = `Question: {question}`;
```

### Few-Shot Examples
```typescript
const prompt = `Classify the sentiment of customer feedback.

Examples:
Input: "Love this product!"
Output: positive

Input: "Worst purchase ever"
Output: negative

Input: "It works fine"
Output: neutral

Input: "${customerFeedback}"
Output:`;
```

### Chain of Thought
```typescript
const prompt = `Solve this step by step:

Question: ${question}

Let's think through this:
1. First, identify the key information
2. Then, determine the approach
3. Finally, calculate the answer

Step-by-step solution:`;
```

## API Integration

### OpenAI Pattern
```typescript
import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function chat(messages: Message[]): Promise<string> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages,
    temperature: 0.7,
    max_tokens: 500,
  });

  return response.choices[0].message.content ?? '';
}
```

### Anthropic Pattern
```typescript
import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

async function chat(prompt: string): Promise<string> {
  const response = await anthropic.messages.create({
    model: 'claude-3-opus-20240229',
    max_tokens: 1024,
    messages: [{ role: 'user', content: prompt }],
  });

  return response.content[0].type === 'text'
    ? response.content[0].text
    : '';
}
```

### Streaming Responses
```typescript
async function* streamChat(prompt: string) {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: prompt }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) yield content;
  }
}
```

## RAG (Retrieval-Augmented Generation)

### Basic RAG Pipeline
```typescript
async function ragQuery(question: string): Promise<string> {
  // 1. Embed the question
  const questionEmbedding = await embedText(question);

  // 2. Search vector database
````
END THIRD-PARTY FILE 0d46df5b64e49108

The third-party file has ended. Everything between the 0d46df5b64e49108 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2740 of 5109 bytes. © MoizIbnYousaf · MIT · https://github.com/MoizIbnYousaf/ai-agent-skills/tree/HEAD/skills/llm-application-dev/SKILL.md

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