llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/moizibnyousaf/ai-agent-skills --skill llm-application-dev- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads llm-application-dev automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
LLM Application Development
Prompt Engineering
Structured Prompts
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
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
const prompt = `Solve this step by step:
Question: ${question}
Let's think through this:
1. First, identify the key information
2. Then, determine the approach
3. Finally, calculate the answer
Step-by-step solution:`;
API Integration
OpenAI Pattern
import OpenAI from 'openai';
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
async function chat(messages: Message[]): Promise<string> {
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages,
temperature: 0.7,
max_tokens: 500,
});
return response.choices[0].message.content ?? '';
}
Anthropic Pattern
import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
async function chat(prompt: string): Promise<string> {
const response = await anthropic.messages.create({
model: 'claude-3-opus-20240229',
max_tokens: 1024,
messages: [{ role: 'user', content: prompt }],
});
return response.content[0].type === 'text'
? response.content[0].text
: '';
}
Streaming Responses
async function* streamChat(prompt: string) {
const stream = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
stream: true,
});
for await (const chunk of stream) {
const content = chunk.choices[0]?.delta?.content;
if (content) yield content;
}
}
RAG (Retrieval-Augmented Generation)
Basic RAG Pipeline
async function ragQuery(question: string): Promise<string> {
// 1. Embed the question
const questionEmbedding = await embedText(question);
// 2. Search vector database
const relevantDocs = await vectorDb.search(questionEmbedding, { limit: 5 });
// 3. Build context
const context = relevantDocs.map(d => d.content).join('\n\n');
// 4. Generate answer
const prompt = `Answer based on this context:\n${context}\n\nQuestion: ${question}`;
return await chat(prompt);
}
Document Chunking
function chunkDocument(text: string, options: ChunkOptions): string[] {
const { chunkSize = 1000, overlap = 200 } = options;
const chunks: string[] = [];
let start = 0;
while (start < text.length) {
const end = Math.min(start + chunkSize, text.length);
chunks.push(text.slice(start, end));
start += chunkSize - overlap;
}
return chunks;
}
Embedding Storage
// Using Supabase with pgvector
async function storeEmbeddings(docs: Document[]) {
for (const doc of docs) {
const embedding = await embedText(doc.content);
await supabase.from('documents').insert({
content: doc.content,
metadata: doc.metadata,
embedding: embedding, // vector column
});
}
}
async function searchSimilar(query: string, limit = 5) {
const embedding = await embedText(query);
const { data } = await supabase.rpc('match_documents', {
query_embedding: embedding,
match_count: limit,
});
return data;
}
Error Handling
async function safeLLMCall<T>(
fn: () => Promise<T>,
options: { retries?: number; fallback?: T }
): Promise<T> {
const { retries = 3, fallback } = options;
for (let i = 0; i < retries; i++) {
try {
return await fn();
} catch (error) {
if (error.status === 429) {
// Rate limit - exponential backoff
await sleep(Math.pow(2, i) * 1000);
continue;
}
if (i === retries - 1) {
if (fallback !== undefined) return fallback;
throw error;
}
}
}
throw new Error('Max retries exceeded');
}
Best Practices
- Token Management: Track usage and set limits
- Caching: Cache embeddings and common queries
- Evaluation: Test prompts with diverse inputs
- Guardrails: Validate outputs before using
- Logging: Log prompts and responses for debugging
- Cost Control: Use cheaper models for simple tasks
- Latency: Stream responses for better UX
- Privacy: Don't send PII to external APIs
FAQ
What does the llm-application-dev skill do?
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
How do I install llm-application-dev?
Run npx skills add https://github.com/moizibnyousaf/ai-agent-skills --skill llm-application-dev in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-application-dev/ (personal) or .claude/skills/llm-application-dev/ (project).
Which AI agents can use llm-application-dev?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is llm-application-dev safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains llm-application-dev?
It is published by moizibnyousaf in moizibnyousaf/ai-agent-skills. It has 463 recorded installs.
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