prompt-engineering

Q: How do I install prompt-engineering?

Run npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill prompt-engineering 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-engineering/ (personal) or .claude/skills/prompt-engineering/ (project).

by spencerpaulyAI & Agents

Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.

Installs: 420
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill prompt-engineering

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-engineering automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Prompt Engineering

Write prompts that get reliable, high-quality output from LLMs.

Core Principles

Be specific — vague prompts get vague results
Show, don't tell — examples beat instructions
Structure the output — tell the model exactly what format you want
Iterate — prompts are code; test and refine them

Techniques

System Prompts

Set the model's role and constraints:

You are a senior code reviewer. Review the provided code for:
1. Security vulnerabilities
2. Performance issues
3. Readability problems

For each issue found, provide:
- Severity (critical/warning/info)
- Line number
- Description
- Suggested fix

If no issues are found, respond with "No issues found."

Few-Shot Examples

Provide 2-3 examples of input → output:

Convert the user's natural language query to a SQL query.

Example 1:
Input: "How many users signed up last month?"
Output: SELECT COUNT(*) FROM users WHERE created_at >= DATE_TRUNC('month', NOW() - INTERVAL '1 month') AND created_at < DATE_TRUNC('month', NOW());

Example 2:
Input: "Show me the top 5 products by revenue"
Output: SELECT p.name, SUM(o.amount) as revenue FROM products p JOIN orders o ON o.product_id = p.id GROUP BY p.name ORDER BY revenue DESC LIMIT 5;

Now convert this query:
Input: "{user_query}"
Output:

Chain-of-Thought

Ask the model to reason step by step:

Analyze this error and suggest a fix. Think step by step:
1. What does the error message mean?
2. What could cause this error?
3. What is the most likely root cause given the code context?
4. What is the fix?

Structured Output

Request JSON or a specific format:

Respond with a JSON object matching this schema:
{
  "summary": "string - one sentence summary",
  "sentiment": "positive | negative | neutral",
  "key_topics": ["string"],
  "confidence": 0.0-1.0
}

Constraints and Guardrails

Rules:
- Only use information from the provided context
- If you don't know the answer, say "I don't know" — do not guess
- Keep responses under 200 words
- Do not include any PII in your response

Patterns for Code

Code generation:

Write a TypeScript function that {description}.

Requirements:
- {requirement 1}
- {requirement 2}

Use these libraries: {libraries}
Follow this pattern from the codebase: {example}

Code transformation:

Refactor this code to {goal}. Keep the same behavior.
Do not change the public API (function signatures, exports).

Bug fixing:

This code has a bug: {description of bug}

Error: {error message}

Fix the bug. Explain what caused it in a comment.

Anti-Patterns

Too vague: "Make this better" → Be specific about what "better" means
Too long: Giant prompts with everything → Split into focused prompts
Contradictory: "Be concise but thorough" → Pick one or define the tradeoff
No examples: Complex formatting without showing what you want → Add 1-2 examples
Prompt injection risk: Including raw user input without delimiting → Use clear delimiters like <user_input>...</user_input>

Tips

Temperature 0 for deterministic tasks (code, classification), 0.7+ for creative tasks
Test prompts with edge cases, not just the happy path
Version control your prompts — they're as important as code
Use structured output (JSON) when parsing the response programmatically
Shorter prompts often outperform longer ones if they're precise enough

FAQ

What does the prompt-engineering skill do?

Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.

How do I install prompt-engineering?

Run npx skills add https://github.com/spencerpauly/awesome-cursor-skills --skill prompt-engineering 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-engineering/ (personal) or .claude/skills/prompt-engineering/ (project).

Which AI agents can use prompt-engineering?

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-engineering safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains prompt-engineering?

It is published by spencerpauly in spencerpauly/awesome-cursor-skills. It has 420 recorded installs.