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cursor-custom-prompts skill

/plugins/saas-packs/cursor-pack/skills/cursor-custom-prompts

This skill helps you craft effective Cursor AI prompts by applying prompt engineering fundamentals, templates, and best practices for consistent, high-quality

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill cursor-custom-prompts

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Files (9)
SKILL.md
1.7 KB
---
name: "cursor-custom-prompts"
description: |
  Create effective custom prompts for Cursor AI. Triggers on "cursor prompts",
  "prompt engineering cursor", "better cursor prompts", "cursor instructions". Use when working with cursor custom prompts functionality. Trigger with phrases like "cursor custom prompts", "cursor prompts", "cursor".
allowed-tools: "Read, Write, Edit, Bash(cmd:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Cursor Custom Prompts

## Overview

This skill helps you create effective custom prompts for Cursor AI. It covers prompt engineering fundamentals, domain-specific templates, advanced techniques like chain-of-thought prompting, and best practices for consistent, high-quality AI responses.

## Prerequisites

- Cursor IDE with Chat or Composer
- Understanding of prompt engineering basics
- Project with existing patterns to reference
- .cursorrules file for persistent prompts

## Instructions

1. Structure prompt with context, task, constraints
2. Include specific requirements and formats
3. Reference existing patterns with @-mentions
4. Start with simple prompt, iterate for complexity
5. Store effective prompts in .cursorrules
6. Refine based on output quality

## Output

- Effective prompt templates
- Consistent AI output quality
- Reusable prompt patterns
- Project-specific prompt library

## Error Handling

See `{baseDir}/references/errors.md` for comprehensive error handling.

## Examples

See `{baseDir}/references/examples.md` for detailed examples.

## Resources

- [Prompt Engineering Guide](https://cursor.com/docs/prompting)
- [.cursorrules Reference](https://cursor.com/docs/rules)
- [Cursor Community Prompts](https://forum.cursor.com/c/prompts)

Overview

This skill helps you design and refine custom prompts for Cursor AI to produce consistent, high-quality responses tailored to your project. It combines prompt structure patterns, domain-specific templates, and advanced techniques so you can build a reusable prompt library. Use it to standardize instructions across teams and automate prompt storage in .cursorrules.

How this skill works

The skill guides you to structure prompts with context, clear tasks, and explicit constraints, then iterates on outputs to improve quality. It shows how to reference existing project patterns with @-mentions, apply chain-of-thought or stepwise prompting when needed, and store successful prompts in .cursorrules for persistent use. Practical templates and refinement steps make prompts repeatable and project-specific.

When to use it

  • Creating reusable prompt templates for a Cursor project
  • Standardizing instructions across a multi-developer workflow
  • When responses are inconsistent or off-target and need refinement
  • Building domain-specific prompts (legal, medical, code review, etc.)
  • Automating prompt persistence via .cursorrules

Best practices

  • Always include context, the exact task, and explicit output format in the prompt
  • Start with a minimal prompt, evaluate outputs, then add constraints iteratively
  • Use examples and reference patterns with @-mentions to align style and behavior
  • Prefer concrete required fields (e.g., JSON schema, headings) to reduce ambiguity
  • Store proven prompts in .cursorrules and version them alongside code

Example use cases

  • Generate consistent code review summaries using a prompt that requires file-level notes and suggested fixes
  • Create a support-response template that always returns a short answer, suggested next steps, and a follow-up question
  • Compose legal-safe summaries by instructing the assistant to highlight assumptions and cite relevant clauses
  • Build an onboarding assistant that uses project context and a fixed output schema to answer developer questions
  • Refine a dataset labeling prompt to produce standardized tags and confidence scores

FAQ

How do I persist prompts so Cursor always uses them?

Save effective prompts in .cursorrules at the project root and reference them from Composer or Chat so they load automatically.

When should I use chain-of-thought prompting?

Use chain-of-thought for complex reasoning tasks where showing intermediate steps improves correctness, but avoid it if brevity or deterministic output is required.

How do I reduce hallucinations?

Constrain outputs with required formats, ask for sources or citations, provide relevant context, and include verification steps in the prompt.