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 applic
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Jeffallan/claude-skills --skill prompt-engineer -a cursor -y
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.
Questions
How do I install prompt-engineer?
Download skills/prompt-engineer/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does prompt-engineer live?
The publisher keeps it at https://github.com/Jeffallan/claude-skills.
Which agents can use prompt-engineer?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
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.
name: prompt-engineer
description: 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.
license: MIT
metadata:
author:
company:
version: "1.2.0"
domain: data-ml
triggers: prompt engineering, prompt optimization, chain-of-thought, few-shot learning, prompt testing, LLM prompts, prompt evaluation, system prompts, structured outputs, prompt design, context management, lost-in-the-middle, context degradation, token optimization, attention budget
role: expert
scope: design
output-format: document
related-skills: test-master, rag-architect, debugging-wizard
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
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.
Download skills/prompt-engineer/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does prompt-engineer live?
The publisher keeps it at https://github.com/Jeffallan/claude-skills.
Which agents can use prompt-engineer?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.