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Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.
npx skill4agent add spencerpauly/awesome-cursor-skills prompt-engineeringYou 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."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: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?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
}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 responseWrite a TypeScript function that {description}.
Requirements:
- {requirement 1}
- {requirement 2}
Use these libraries: {libraries}
Follow this pattern from the codebase: {example}Refactor this code to {goal}. Keep the same behavior.
Do not change the public API (function signatures, exports).This code has a bug: {description of bug}
Error: {error message}
Fix the bug. Explain what caused it in a comment.<user_input>...</user_input>