prompt-engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-ancoleman-ai-design-components --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.
What it does
Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks.
How it works
- Provides systematic techniques for crafting prompts that elicit desired behaviors from LLMs (zero-shot, few-shot, chain-of-thought, structured outputs).
- Covers multi-model deployment (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models) with Python and TypeScript examples.
- Includes a Quick Start with examples for zero-shot prompting (Python) and structured output generation (TypeScript).
- Offers a Prompting Technique Decision Framework and a set of Core Prompting Patterns (Zero-Shot, Chain-of-Thought, Few-Shot, Structured Output Generation, System Prompts and Personas, Tool Use and Function Calling, Prompt Chaining and Composition).
- Provides library recommendations for Python and TypeScript ecosystems and production best practices (versioning, cost tracking, error handling, input sanitization, testing).
- Describes how to select techniques based on task goals and constraints.
When to use it
- Trigger this skill when building LLM-powered applications requiring consistent outputs
- Use when model outputs are unreliable or hallucinating
- When structured data (JSON) is needed from natural language inputs
- For multi-step reasoning tasks or agent behaviors that use tools/APIs
- To optimize prompt costs or latency in production systems
- When migrating prompts across model providers and establishing versioning/testing workflows
What it can touch
- Tool use and function calling patterns are described for OpenAI function calling and Anthropic tooling, including examples of defining functions, tools, and how the model decides when to call them.
Caveats
- License is MIT.
- Describes best practices and patterns but does not guarantee outcomes; users should implement and validate prompts in their environment.
# Prompt Engineering Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks. ## Purpose This skill provides systematic techniques for crafting prompts that consistently elicit desired behaviors from LLMs. Rather than trial-and-error prompt iteration, apply proven patterns (zero-shot, few-shot, chain-of-thought, structured outputs) to improve accuracy, reduce costs, and build production-ready LLM applications. Covers multi-model deployment (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models) with Python and TypeScript examples. ## When to Use This Skill **Trigger this skill when:** - Building LLM-powered applications requiring consistent outputs - Model outputs are unreliable, inconsistent, or hallucinating - Need structured data (JSON) from natural language inputs - Implementing multi-step reasoning tasks (math, logic, analysis) - Creating AI agents that use tools and external APIs - Optimizing prompt costs or latency in production systems - Migrating prompts across different model providers - Establishing prompt versioning and testing workflows **Common requests:** - "How do I make Claude/GPT follow
- Purpose
- When to Use This Skill
- Quick Start
- Prompting Technique Decision Framework
- Core Prompting Patterns
- 1. Zero-Shot Prompting
- 2. Chain-of-Thought (CoT)
- 3. Few-Shot Learning
- 4. Structured Output Generation
- 5. System Prompts and Personas
- 6. Tool Use and Function Calling
- 7. Prompt Chaining and Composition
- Library Recommendations
- Python Ecosystem
What does the prompt-engineering skill do?
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-ancoleman-ai-design-components --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.