prompt-engeneering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.
npx skills add majiayu000/claude-skill-registry --skill prompt-engeneering --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 Universal techniques for crafting effective prompts across any LLM. ## Core Principles ### 1. Structure with XML Tags Use XML tags to create clear, parseable prompts: ```xml <context>Background information here</context> <instructions> 1. First step 2. Second step </instructions> <examples>Sample inputs/outputs</examples> <output_format>Expected structure</output_format> ``` **Benefits:** - **Clarity**: Separates context, instructions, and examples - **Accuracy**: Prevents model from mixing up sections - **Flexibility**: Easy to modify individual parts - **Parseability**: Enables structured output extraction **Best practices:** - Use consistent tag names throughout (`<instructions>`, not sometimes `<steps>`) - Reference tags explicitly: "Using the data in `<context>` tags..." - Nest tags for hierarchy: `<examples><example id="1">...</example></examples>` - Combine with other techniques: `<thinking>` for chain-of-thought, `<answer>` for final output ### 2. Control Output Shape Specify explicit constraints on length, format, and structure: ```xml <output_spec> - Default: 3-6 sentences or ≤5 bullets - Simple yes/no questions: ≤2 sentences - Complex multi-step task
- Core Principles
- 1. Structure with XML Tags
- 2. Control Output Shape
- 3. Prevent Scope Drift
- 4. Handle Ambiguity Explicitly
- 5. Long-Context Grounding
- Agentic Prompts
- Tool Usage Rules
- User Updates
- Self-Check for High-Risk Outputs
- Structured Extraction
- Web Research Prompts
- Example: Before/After
- Prompt Migration Checklist
What does the prompt-engeneering skill do?
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engeneering --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.