llm-application-patterns
This skill should be used when building production LLM applications in any language. It applies when implementing predictable AI features, creating structured interfaces for LLM operations, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality. Covers language-agnostic patterns for type-safe contracts, modular composition, multi-provider support, and production deployment.
npx skills add majiayu000/claude-skill-registry --skill llm-application-patterns --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM Application Patterns ## Overview Build production LLM applications using structured, testable patterns. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code. **Core principle: Program LLMs, don't prompt them.** This skill provides language-agnostic guidance on: - Creating type-safe signatures for LLM operations - Building composable modules and workflows - Configuring multiple LLM providers - Implementing agents with tools - Testing and optimizing LLM applications - Production deployment patterns ## Core Concepts ### 1. Type-Safe Signatures Define input/output contracts for LLM operations with runtime type checking. **When to use**: Any LLM task, from simple classification to complex analysis. **Pattern** (pseudo-code): ``` Signature: EmailClassification Description: "Classify customer support emails" Inputs: email_subject: String (required) email_body: String (required) Outputs: category: Enum["Technical", "Billing", "General"] priority: Enum["Low", "Medium", "High"] confidence: Float (0.0 to 1.0) ``` **Best practices**: - Always provide clear, speci
- Overview
- Core Concepts
- 1. Type-Safe Signatures
- 2. Composable Modules
- 3. Predictor Types
- 4. Provider Configuration
- Common Patterns
- Multi-Step Pipeline
- Agent with Tools
- Conditional Router
- Retry with Fallback
- Quick Start Workflow
- 1. Define Your Signature
- 2. Create a Module
What does the llm-application-patterns skill do?
This skill should be used when building production LLM applications in any language. It applies when implementing predictable AI features, creating structured interfaces for LLM operations, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality. Covers language-agnostic patterns for type-safe contracts, modular composition, multi-provider support, and production deployment.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-application-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.