Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
npxskills add skillcreatorai/ai-agent-skills--skill llm-application-devLoading…
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}`;
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:`;
const prompt = `Solve this step by step:
Question: ${question}
Let's think through this:
1. First, identify the key information
2. Then, determine the approach
3. Finally, calculate the answer
Step-by-step solution:`;
import OpenAI from 'openai';
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
async function chat(messages: Message[]): Promise<string> {
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages,
temperature: 0.7,
max_tokens: 500,
});
return response.choices[0].message.content ?? '';
}
import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
async function chat(prompt: string): Promise<string> {
const response = await anthropic.messages.create({
model: 'claude-3-opus-20240229',
max_tokens: 1024,
messages: [{ role: 'user', content: prompt }],
});
return response.content[0].type === 'text'
? response.content[0].text
: '';
}
async function* streamChat(prompt: string) {
const stream = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
stream: true,
});
for await (const chunk of stream) {
const content = chunk.choices[0]?.delta?.content;
if (content) yield content;
}
}
async function ragQuery(question: string): Promise<string> {
// 1. Embed the question
const questionEmbedding = await embedText(question);
// 2. Search vector database
const relevantDocs = await vectorDb.search(questionEmbedding, { limit: 5 });
// 3. Build context
const context = relevantDocs.map(d => d.content).join('\n\n');
// 4. Generate answer
const prompt = `Answer based on this context:\n${context}\n\nQuestion: ${question}`;
return await chat(prompt);
}
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;
}
// 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;
}
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');
}
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).