prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
npx skills add Jeffallan/claude-skills --skill prompt-engineer --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 Engineer Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases. ## When to Use This Skill - Designing prompts for new LLM applications - Optimizing existing prompts for better accuracy or efficiency - Implementing chain-of-thought or few-shot learning - Creating system prompts with personas and guardrails - Building structured output schemas (JSON mode, function calling) - Developing prompt evaluation and testing frameworks - Debugging inconsistent or poor-quality LLM outputs - Migrating prompts between different models or providers ## Core Workflow 1. **Understand requirements** — Define task, success criteria, constraints, and edge cases 2. **Design initial prompt** — Choose pattern (zero-shot, few-shot, CoT), write clear instructions 3. **Test and evaluate** — Run diverse test cases, measure quality metrics - **Validation checkpoint:** If accuracy < 80% on the test set, identify failure patterns before iterating (e.g., ambiguous instructions, missing examples, edge case gaps) 4. **Iterate and optimize** — Make one change at a time; refine based on failures, reduce tokens, improve relia
- When to Use This Skill
- Core Workflow
- Reference Guide
- Prompt Examples
- Zero-shot vs. Few-shot
- Before/After Optimization
- Constraints
- MUST DO
- MUST NOT DO
- Output Templates
- Coverage Note
What does the prompt-engineer skill do?
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
How do I install it?
Run `npx skills add Jeffallan/claude-skills --skill prompt-engineer --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 Jeffallan/claude-skills, a repository with 10,871 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.