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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill prompt-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads prompt-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Understand requirements — Define task, success criteria, constraints, and edge cases
- Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
- 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)
- Iterate and optimize — Make one change at a time; refine based on failures, reduce tokens, improve reliability
- Document and deploy — Version prompts, document behavior, monitor production
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Prompt Patterns | references/prompt-patterns.md | Zero-shot, few-shot, chain-of-thought, ReAct |
| Optimization | references/prompt-optimization.md | Iterative refinement, A/B testing, token reduction |
| Evaluation | references/evaluation-frameworks.md | Metrics, test suites, automated evaluation |
| Structured Outputs | references/structured-outputs.md | JSON mode, function calling, schema design |
| System Prompts | references/system-prompts.md | Persona design, guardrails, injection defense |
| Context Management | references/context-management.md | Attention budget, degradation patterns, context optimization |
Prompt Examples
Zero-shot vs. Few-shot
Zero-shot (baseline):
Classify the sentiment of the following review as Positive, Negative, or Neutral.
Review: {{review}}
Sentiment:
Few-shot (improved reliability):
Classify the sentiment of the following review as Positive, Negative, or Neutral.
Review: "The battery life is incredible, lasts all day."
Sentiment: Positive
Review: "Stopped working after two weeks. Very disappointed."
Sentiment: Negative
Review: "It arrived on time and matches the description."
Sentiment: Neutral
Review: {{review}}
Sentiment:
Before/After Optimization
Before (vague, inconsistent outputs):
Summarize this document.
{{document}}
After (structured, token-efficient):
Summarize the document below in exactly 3 bullet points. Each bullet must be one sentence and start with an action verb. Do not include opinions or information not present in the document.
Document:
{{document}}
Summary:
Constraints
MUST DO
- Test prompts with diverse, realistic inputs including edge cases
- Measure performance with quantitative metrics (accuracy, consistency)
- Version prompts and track changes systematically
- Document expected behavior and known limitations
- Use few-shot examples that match target distribution
- Validate structured outputs against schemas
- Consider token costs and latency in design
- Test across model versions before production deployment
MUST NOT DO
- Deploy prompts without systematic evaluation on test cases
- Use few-shot examples that contradict instructions
- Ignore model-specific capabilities and limitations
- Skip edge case testing (empty inputs, unusual formats)
- Make multiple changes simultaneously when debugging
- Hardcode sensitive data in prompts or examples
- Assume prompts transfer perfectly between models
- Neglect monitoring for prompt degradation in production
Output Templates
When delivering prompt work, provide:
- Final prompt with clear sections (role, task, constraints, format)
- Test cases and evaluation results
- Usage instructions (temperature, max tokens, model version)
- Performance metrics and comparison with baselines
- Known limitations and edge cases
Coverage Note
Reference files cover major prompting techniques (zero-shot, few-shot, CoT, ReAct, tree-of-thoughts), structured output patterns (JSON mode, function calling), context management (attention budgets, degradation mitigation, optimization), and model-specific guidance for GPT-4, Claude, and Gemini families. Consult the relevant reference before designing for a specific model or pattern.
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
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 prompt-engineer?
Run npx skills add https://github.com/jeffallan/claude-skills --skill prompt-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-engineer/ (personal) or .claude/skills/prompt-engineer/ (project).
Which AI agents can use prompt-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is prompt-engineer safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains prompt-engineer?
It is published by jeffallan in jeffallan/claude-skills. It has 5,642 recorded installs.
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