Agent skill

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.

View SKILL.md on GitHub Repository
Stars 987
Forks 112

Install this agent skill to your Project

npx add-skill https://github.com/MoizIbnYousaf/Ai-Agent-Skills/tree/main/skills/llm-application-dev

SKILL.md

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
  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

typescript
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

typescript
// 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

typescript
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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

MoizIbnYousaf/Ai-Agent-Skills

content-research-writer

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

987 112
Explore
MoizIbnYousaf/Ai-Agent-Skills

best-practices

Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.

987 112
Explore
MoizIbnYousaf/Ai-Agent-Skills

backend-development

Backend API design, database architecture, microservices patterns, and test-driven development. Use for designing APIs, database schemas, or backend system architecture.

987 112
Explore
MoizIbnYousaf/Ai-Agent-Skills

ask-questions-if-underspecified

Clarify requirements before implementing. Do not use automatically, only when invoked explicitly.

987 112
Explore
MoizIbnYousaf/Ai-Agent-Skills

code-documentation

Writing effective code documentation - API docs, README files, inline comments, and technical guides. Use for documenting codebases, APIs, or writing developer guides.

987 112
Explore
MoizIbnYousaf/Ai-Agent-Skills

changelog-generator

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

987 112
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results