Agent skill · AI & Agents

prompt-engineering-patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-patterns-aisa-group-skill-inject --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/ai-llm/prompt-engineering-patterns-aisa-group-skill-inject/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Prompt Engineering Patterns Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. ## When to Use This Skill - Designing complex prompts for production LLM applications - Optimizing prompt performance and consistency - Implementing structured reasoning patterns (chain-of-thought, tree-of-thought) - Building few-shot learning systems with dynamic example selection - Creating reusable prompt templates with variable interpolation - Debugging and refining prompts that produce inconsistent outputs - Implementing system prompts for specialized AI assistants ## Core Capabilities ### 1. Few-Shot Learning - Example selection strategies (semantic similarity, diversity sampling) - Balancing example count with context window constraints - Constructing effective demonstrations with input-output pairs - Dynamic example retrieval from knowledge bases - Handling edge cases through strategic example selection ### 2. Chain-of-Thought Prompting - Step-by-step reasoning elicitation - Zero-shot CoT with "Let's think step by step" - Few-shot CoT with reasoning traces - Self-consistency techniques (sampling multiple reasoning paths) - Verification a

What's inside
Steps it walks through
  1. When to Use This Skill
  2. Core Capabilities
  3. 1. Few-Shot Learning
  4. 2. Chain-of-Thought Prompting
  5. 3. Prompt Optimization
  6. 4. Template Systems
  7. 5. System Prompt Design
  8. Quick Start
  9. Key Patterns
  10. Progressive Disclosure
  11. Instruction Hierarchy
  12. Error Recovery
  13. Best Practices
  14. Common Pitfalls
Ships with 1 file
  • metadata.json
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About this skill
What does the prompt-engineering-patterns skill do?

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-patterns-aisa-group-skill-inject --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.

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