prompt-engineering
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, designing production prompt templates, or building AI-powered features.
npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-wpank-ai --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.
# Prompt Engineering Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. ## Installation ### OpenClaw / Moltbot / Clawbot ```bash npx clawhub@latest install prompt-engineering ``` ## When to Use - 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 prompts that produce inconsistent outputs - Implementing system prompts for specialized AI assistants - Using structured outputs (JSON mode) for reliable parsing ## Core Techniques ### 1. Few-Shot Learning Provide examples that demonstrate the desired behavior: - **Semantic similarity** — select examples closest to the input - **Diversity sampling** — cover the range of expected inputs - **Balance count vs context** — more examples aren't always better; respect the context window - **Dynamic retrieval** — pull examples from a knowledge base at runtime **For patterns and implementation**: See `references/few
- Installation
- OpenClaw / Moltbot / Clawbot
- When to Use
- Core Techniques
- 1. Few-Shot Learning
- 2. Chain-of-Thought Prompting
- 3. Structured Outputs
- 4. System Prompt Design
- 5. Template Systems
- Key Patterns
- Pattern 1: Structured Output with Validation
- Pattern 2: Chain-of-Thought with Self-Verification
- Pattern 3: Progressive Disclosure
- Pattern 4: Error Recovery and Fallback
npx clawhub@latest install prompt-engineering
What does the prompt-engineering skill do?
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, designing production prompt templates, or building AI-powered features.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-wpank-ai --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.