prompt-engineering
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-absolutelyskilled-absolutelyskilled --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.
When this skill is activated, always start your first response with the 🧢 emoji. # Prompt Engineering Prompt engineering is the practice of designing inputs to language models to reliably elicit high-quality, accurate, and appropriately formatted outputs. It covers everything from writing system instructions to multi-step reasoning pipelines and retrieval-augmented generation. Effective prompting reduces hallucinations, improves consistency, and unlocks capabilities the model already has but needs guidance to apply. The techniques here apply across providers (OpenAI, Anthropic, Google) with minor syntactic differences. --- ## When to use this skill Trigger this skill when the task involves: - Writing or refining a system prompt for an agent or chatbot - Implementing chain-of-thought reasoning to improve accuracy on hard tasks - Designing few-shot examples to steer model behavior - Building a RAG pipeline (retrieval + context injection + generation) - Getting structured JSON/schema output from a model reliably - Chaining multiple LLM calls (decomposition, routing, verification) - Evaluating or benchmarking prompt quality across dimensions - Choosing between zero-shot, few-shot, fin
- When to use this skill
- Key principles
- Core concepts
- System / user / assistant roles
- Temperature and sampling
- Token economics
- Context window management
- Prompt vs fine-tuning decision
- Common tasks
- Write effective system prompts
- Implement chain-of-thought
- Design few-shot examples
- Build a RAG prompt pipeline
- Get structured JSON output
What does the prompt-engineering skill do?
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-absolutelyskilled-absolutelyskilled --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.