llm-app-patterns

Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.

davila7/claude-code-templates|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

Installation Guide

Select your AI agent

How to use llm-app-patterns on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add llm-app-patterns

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill llm-app-patterns

Fetches llm-app-patterns from davila7/claude-code-templates and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/llm-app-patterns

Restart Cursor to activate llm-app-patterns. Access via /llm-app-patterns in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

1. RAG Pipeline Architecture

Overview

RAG (Retrieval-Augmented Generation) grounds LLM responses in your data.

┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Ingest │────▶│ Retrieve │────▶│ Generate │ │ Documents │ │ Context │ │ Response │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────┐ ┌───────────┐ ┌───────────┐ │ Chunking│ │ Vector │ │ LLM │ │Embedding│ │ Search │ │ + Context│ └─────────┘ └───────────┘ └───────────┘

1.1 Document Ingestion

# Chunking strategies class ChunkingStrategy: # Fixed-size chunks (simple but may break context) FIXED_SIZE = "fixed_size" # e.g., 512 tokens # Semantic chunking (preserves meaning) SEMANTIC = "semantic" # Split on paragraphs/sections # Recursive splitting (tries multiple separators) RECURSIVE = "recursive" # ["\n\n", "\n", " ", ""] # Document-aware (respects structure) DOCUMENT_AWARE = "document_aware" # Headers, lists, etc. # Recommended settings CHUNK_CONFIG = { "chunk_size": 512, # tokens "chunk_overlap": 50, # token overlap between chunks "separators": ["\n\n", "\n", ". ", " "], }

1.2 Embedding & Storage

# Vector database selection VECTOR_DB_OPTIONS = { "pinecone": { "use_case": "Production, managed service", "scale": "Billions of vectors", "features": ["Hybrid search", "Metadata filtering"] }, "weaviate": { "use_case": "Self-hosted, multi-modal", "scale": "Millions of vectors", "features": ["GraphQL API", "Modules"] }, "chromadb": { "use_case": "Development, prototyping", "scale": "Thousands of vectors", "features": ["Simple API", "In-memory option"] }, "pgvector": { "use_case": "Existing Postgres infrastructure", "scale": "Millions of vectors", "features": ["SQL integration", "ACID compliance"] } } # Embedding model selection EMBEDDING_MODELS = { "openai/text-embedding-3-small": { "dimensions": 1536, "cost": "$0.02/1M tokens", "quality": "Good for most use cases" }, "openai/text-embedding-3-large": { "dimensions": 3072, "cost": "$0.13/1M tokens", "quality": "Best for complex queries" }, "local/bge-large": { "dimensions": 1024, "cost": "Free (compute only)", "quality": "Comparable to OpenAI small" } }

1.3 Retrieval Strategies

# Basic semantic search def semantic_search(query: str, top_k: int = 5): query_embedding = embed(query) results = vector_db.similarity_search( query_embedding, top_k=top_k ) return results # Hybrid search (semantic + keyword) def hybrid_search(query: str, top_k: int = 5, alpha: float = 0.5): """ alpha=1.0: Pure semantic alpha=0.0: Pure keyword (BM25) alpha=0.5: Balanced """ semantic_results = vector_db.similarity_search(query) keyword_results = bm25_search(query) # Reciprocal Rank Fusion return rrf_merge(semantic_results, keyword_results, alpha) # Multi-query retrieval def multi_query_retrieval(query: str): """Generate multiple query variations for better recall""" queries = llm.generate_query_variations(query, n=3) all_results = [] for q in queries: all_results.extend(semantic_search(q)) return deduplicate(all_results) # Contextual compression def compressed_retrieval(query: str): """Retrieve then compress to relevant parts only""" docs = semantic_search(query, top_k=10) compressed = llm.extract_relevant_parts(docs, query) return compressed

1.4 Generation with Context

RAG_PROMPT_TEMPLATE = """ Answer the user's question based ONLY on the following context. If the context doesn't contain enough information, say "I don't have enough information to answer that." Context: {context} Question: {question} Answer:""" def generate_with_rag(question: str): # Retrieve context_docs = hybrid_search(question, top_k=5) context = "\n\n".join([doc.content for doc in context_docs]) # Generate prompt = RAG_PROMPT_TEMPLATE.format( context=context, question=question ) response = llm.generate(prompt) # Return with citations return { "answer": response, "sources": [doc.metadata for doc in context_docs] }

2. Agent Architectures

2.1 ReAct Pattern (Reasoning + Acting)

Thought: I need to search for information about X Action: search("X") Observation: [search results] Thought: Based on the results, I should... Action: calculate(...) Observation: [calculation result] Thought: I now have enough information Action: final_answer("The answer is...")

llm-app-patterns

Installation Guide

How to use llm-app-patterns on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

Task Automation & Efficiency

Knowledge Enhancement

Quality Improvement

Install Skill

🤖 LLM Application Patterns

When to Use This Skill

1. RAG Pipeline Architecture

Overview

1.1 Document Ingestion

1.2 Embedding & Storage

1.3 Retrieval Strategies

1.4 Generation with Context

2. Agent Architectures

2.1 ReAct Pattern (Reasoning + Acting)

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

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`Reviews`

`Discussion`