Guide · Agent skill
How to use the Prompt Engineer agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The prompt-engineer skill is an add-on for an AI coding agent that helps you design, refactor and test prompts for large language models. It suits developers building an LLM-powered feature and anyone who needs a repeatable way to measure whether a prompt works. After this guide you will know how to install the skill, how to phrase requests that trigger it, and what kinds of output to expect.
Prompt EngineerJeffallan/claude-skills · MIT Download the skill Quick start
- 1Download the ZIP. Get the prompt-engineer skill ZIP from the Jeffallan/claude-skills page in this directory.
- 2Unzip the skill folder. Extract it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder.
- 3Or upload in the Claude apps. Go to Settings → Capabilities → Skills and upload the ZIP.
- 4Describe your prompt task. Ask in plain language, for example to design a system prompt or refactor an existing prompt.
- 5Supply examples and success criteria. Share sample inputs, expected outputs and what counts as a good result.
- 6Test and iterate. Run the proposed prompt on test cases and change one thing at a time based on failures.
What is an agent skill and what does this one do?
An agent skill is a folder of instructions and reference files that an AI agent loads when a task matches its description. This skill is documentation-driven: its SKILL.md file points to reference guides on prompt patterns, optimization, evaluation, structured outputs, system prompts and context management, and the agent reads them as needed.
Once installed, the agent approaches prompt writing methodically. It drafts prompts with clear sections for role, task, constraints and format, picks a pattern such as zero-shot, few-shot or chain-of-thought, and designs JSON output schemas and system prompts with guardrails. It can also help build test cases and scoring rubrics, and when debugging it suggests changing one thing at a time.
- Writes and refactors prompt templates
- Designs structured output and JSON or function-calling schemas
- Drafts system prompts with personas and guardrails
- Builds evaluation rubrics and test suites
Install the prompt-engineer skill
Download the ZIP for the skill from the Jeffallan/claude-skills page in this directory (skills/prompt-engineer, licence MIT). Then choose one of two install routes.
For a personal install, unzip the folder into ~/.claude/skills/. For a single project, unzip it into the project's .claude/skills/ folder. Alternatively, upload the ZIP in the Claude apps under Settings → Capabilities → Skills.
- Personal: unzip into ~/.claude/skills/
- Single project: unzip into .claude/skills/ inside the project
- Claude apps: Settings → Capabilities → Skills, then upload the ZIP
Trigger the skill with natural requests
You do not need a special command. Describe the prompt task in plain language and the agent will match it against the skill's description. The description covers designing prompts for new LLM applications, refactoring prompts for accuracy or token efficiency, chain-of-thought or few-shot learning, system prompts with personas and guardrails, JSON or function-calling schemas, and prompt evaluation frameworks.
Name the outcome you want. Requests such as 'refactor this prompt so it returns consistent output' or 'write a rubric to score my summarisation prompt' give the agent enough to work with.
- "Design a system prompt with a persona and guardrails for my support chatbot."
- "Refactor this extraction prompt to be more token-efficient and consistent."
- "Write a JSON output schema and five test cases for this classifier prompt."
- "Use few-shot examples to improve this sentiment prompt."
Examples: what the skill produces
The skill's own examples show the difference between a baseline and an improved prompt. A zero-shot sentiment prompt gives the model only an instruction and a review. The few-shot version adds three labelled examples, one each for Positive, Negative and Neutral, which the skill notes improves reliability.
For vague prompts, the skill tightens the instruction. A bare 'Summarize this document' becomes a request for exactly 3 bullet points, each one sentence starting with an action verb, with no information absent from the document.
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:What the skill needs and what it does not do
The skill lists no requirements. It does not run scripts and does not use the network, according to its metadata. It works from text: you provide the prompt, task and examples, and the agent returns drafts and reasoning.
The skill gives method and examples, not proof. It cannot guarantee that a prompt will work on your data or on future model versions. Its own rules say to test across model versions before production and to keep test sets realistic, including edge cases such as empty inputs.
- Requirements listed: none
- Runs scripts: no
- Uses network: no
Tips for getting reliable results
Give the agent your success criteria and a few real inputs, including awkward ones. The skill's workflow starts with defining task, success criteria, constraints and edge cases, then designs, tests and iterates.
When a prompt misbehaves, change one thing at a time. The skill's checkpoint says that if accuracy is below 80% on the test set, you should identify failure patterns before iterating. Keep your API keys and personal data out of prompts and examples.
- Provide real sample inputs and expected outputs
- Ask for a test set with edge cases before deploying
- Change one variable per iteration when debugging
- Record temperature, max tokens and model version with each prompt
What people use it for
- Designing a system prompt with a persona and guardrails for a customer support assistant
- Refactoring an inconsistent extraction prompt for more reliable output
- Writing a JSON output schema for a function-calling integration
- Building a scoring rubric and test suite to compare two prompt versions
Frequently asked questions
How do I install the prompt-engineer skill?
Download the ZIP from the Jeffallan/claude-skills page in this directory. Unzip it into ~/.claude/skills/ for personal use or into a project's .claude/skills/ folder, or upload the ZIP under Settings → Capabilities → Skills in the Claude apps.
Does the prompt-engineer skill have any requirements?
The skill lists no requirements. Its metadata states that it does not run scripts and does not use the network.
Is the prompt-engineer skill free?
The skill is released under the MIT licence, as stated in its SKILL.md. This page does not list pricing, so check the licence and any platform terms for the app you use.
Will this skill guarantee my prompt works?
No. The skill provides method, templates and examples, but it cannot prove a prompt will perform on your data or on future model versions. You still need to run your own test cases.
When should I not use this skill?
Skip it for quick one-off questions where a simple prompt is enough, because the workflow adds process overhead. It is aimed at building and testing prompts systematically.
What is the difference between zero-shot and few-shot prompting?
Zero-shot gives the model an instruction with no examples. Few-shot adds labelled examples before the real input, which the skill's example shows improving reliability for a sentiment task.
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Our verdict
Use this skill if you are building an LLM feature and want a systematic process for designing, testing and tightening prompts. Pick something else if you only need a quick one-off prompt, or if you expect a tool to guarantee accuracy on your data.
Download the skill