home / skills / amnadtaowsoam / cerebraskills / prompt-library-minimal

This skill helps you craft minimal, cost-efficient prompts for coding tasks, speeding responses while maintaining quality.

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---
name: Prompt Library Minimal
description: Library of minimal prompts that use the fewest tokens while delivering good results, with templates and best practices
---

# Prompt Library Minimal

## Overview

Library of optimized prompts that use minimal tokens while maintaining effectiveness - suitable for production environments that need to reduce costs.

## Why This Matters

- **Cost savings**: Reduce token usage 50-70%
- **Speed**: Fewer tokens = faster responses
- **Proven**: Tested and production-ready
- **Reusable**: Copy-paste ready

---

## Code Review

### Verbose (❌ 85 tokens)
```
I would like you to please review this code carefully and provide
detailed feedback on potential bugs, performance issues, security
vulnerabilities, and code quality concerns. Please also suggest
improvements and best practices that could be applied.
```

### Minimal (✅ 12 tokens)
```
Review for bugs, performance, security. Suggest improvements.
```

**Savings: 86%**

---

## Bug Fix

### Verbose (❌ 120 tokens)
```
I'm experiencing an issue where the login function is not working
properly. When users try to log in, they receive an error message.
Could you please help me identify what might be causing this problem
and suggest a solution? Here is the relevant code...
```

### Minimal (✅ 18 tokens)
```
Login fails with error. Fix:
[code]

Expected: successful login
Actual: error message
```

**Savings: 85%**

---

## Code Generation

### Verbose (❌ 95 tokens)
```
Please write a function that will calculate the sum of two numbers.
The function should accept two parameters and return their sum.
Please include proper error handling and add comments explaining
what the code does.
```

### Minimal (✅ 15 tokens)
```
Function: sum two numbers
Include: error handling, comments
Language: TypeScript
```

**Savings: 84%**

---

## Refactoring

### Verbose (❌ 110 tokens)
```
I have this code that works but I think it could be improved.
Could you please refactor it to make it more readable, maintainable,
and efficient? Please follow best practices and modern coding standards.
Also, please explain the changes you make.
```

### Minimal (✅ 8 tokens)
```
Refactor for readability, efficiency:
[code]
```

**Savings: 93%**

---

## Documentation

### Verbose (❌ 75 tokens)
```
Please write comprehensive documentation for this function including
a description of what it does, the parameters it accepts, what it
returns, and provide usage examples.
```

### Minimal (✅ 6 tokens)
```
Document function:
[code]
```

**Savings: 92%**

---

## Testing

### Verbose (❌ 90 tokens)
```
I need you to write unit tests for this function. The tests should
cover normal cases, edge cases, and error cases. Please use Jest
as the testing framework and follow testing best practices.
```

### Minimal (✅ 10 tokens)
```
Jest tests (normal, edge, error):
[code]
```

**Savings: 89%**

---

## Debugging

### Verbose (❌ 100 tokens)
```
I'm getting an error in my code and I can't figure out what's wrong.
The error message says "Cannot read property 'name' of undefined".
Can you help me understand what's causing this and how to fix it?
```

### Minimal (✅ 12 tokens)
```
Error: Cannot read property 'name' of undefined
Code: [snippet]
Fix?
```

**Savings: 88%**

---

## Optimization

### Verbose (❌ 85 tokens)
```
This code is running slowly and I need to optimize it for better
performance. Can you analyze it and suggest ways to make it faster?
Please focus on algorithmic improvements and best practices.
```

### Minimal (✅ 8 tokens)
```
Optimize for speed:
[code]
```

**Savings: 91%**

---

## Template Library

### Code Review Template
```
Review for [aspects]:
[code]
```

### Bug Fix Template
```
[Issue] fails with [error]
Code: [snippet]
Expected: [behavior]
Actual: [behavior]
```

### Feature Request Template
```
Add [feature]:
- Input: [description]
- Output: [description]
- Edge cases: [list]
```

### Refactor Template
```
Refactor for [goals]:
[code]
```

### Test Template
```
[Framework] tests ([cases]):
[code]
```

---

## Output Format Specifications

### JSON Output
```
Output JSON:
{
  "field1": "...",
  "field2": "..."
}
```

### List Output
```
List 5 items:
1. ...
```

### Code Only
```
Code only, no explanation:
[requirements]
```

### Summary
```
Summary (≤50 words):
[content]
```

---

## Constraints

### Length Constraints
```
Max 100 words
Max 10 lines
≤3 bullet points
```

### Format Constraints
```
TypeScript only
No comments
Use async/await
```

### Style Constraints
```
Functional style
No loops (use map/filter)
Immutable
```

---

## Best Practices

### 1. Use Imperative Mood
```
❌ "Could you please write..."
✅ "Write..."

❌ "I would like you to..."
✅ "Create..."
```

### 2. Specify Output Format
```
❌ "Explain this code"
✅ "Explain in 3 bullet points"
```

### 3. Be Specific
```
❌ "Make it better"
✅ "Optimize for speed"
```

### 4. Remove Pleasantries
```
❌ "Thank you for your help"
✅ [omit]

❌ "Please and thank you"
✅ [omit]
```

### 5. Use Abbreviations
```
❌ "TypeScript"
✅ "TS"

❌ "JavaScript"
✅ "JS"

❌ "database"
✅ "DB"
```

---

## Quick Reference

### Common Tasks

| Task | Minimal Prompt | Tokens |
|------|---------------|--------|
| Code review | `Review for bugs, performance:` | 4 |
| Bug fix | `Fix: [error]` | 2 |
| Generate | `Function: [description]` | 2 |
| Refactor | `Refactor for [goal]:` | 3 |
| Test | `Tests ([cases]):` | 2 |
| Document | `Document:` | 1 |
| Optimize | `Optimize for [metric]:` | 3 |
| Debug | `Error: [message]. Fix?` | 3 |

---

## Measurement

### Before Optimization
```
Average prompt: 95 tokens
Cost per 1000 requests: $4.75
```

### After Optimization
```
Average prompt: 15 tokens
Cost per 1000 requests: $0.75
Savings: 84%
```

---

## Summary

**Prompt Library Minimal:** Prompts that use minimal tokens

**Savings:**
- Code review: 86%
- Bug fix: 85%
- Generation: 84%
- Refactoring: 93%
- Documentation: 92%

**Principles:**
- Imperative mood
- No pleasantries
- Specify format
- Be specific
- Use abbreviations

**Average savings: 85-90%**

**Usage:**
```
Copy template → Fill in details → Use
```

Overview

This skill provides a library of minimal, token-efficient prompts and templates designed to produce reliable results while reducing API cost and latency. It focuses on concise, production-ready prompts for common developer tasks like code review, bug fixing, generation, refactoring, testing, and documentation. The goal is repeatable savings and faster responses without sacrificing output quality.

How this skill works

The library gives short imperative templates and examples that replace long natural-language requests with focused instructions and required fields. It supplies format and constraint patterns (JSON, code-only, length limits) and combines them with best-practice heuristics like specifying output shape and removing pleasantries. Copy a template, fill the placeholders, and send it to your model to get consistent, low-token responses.

When to use it

  • When you need to reduce token costs and latency in production
  • When you want predictable, repeatable prompt outputs
  • When sending high-volume requests (CI, bots, batch jobs)
  • When you require strict output formats (JSON, code-only)
  • When you want compact templates for developer workflows

Best practices

  • Write commands in imperative mood (e.g., "Refactor for readability")
  • Specify exact output format and constraints (fields, length, language)
  • Include only essential context and a minimal example or code snippet
  • Omit pleasantries and filler words to save tokens
  • Standardize templates across your team for repeatability

Example use cases

  • Automated code review: use "Review for bugs, performance, security" plus code snippet
  • Bug triage: "[Issue] fails with [error] Code: [snippet] Expected: [behavior]"
  • Generate functions: "Function: sum two numbers Include: error handling, comments Language: TS"
  • Refactor tasks: "Refactor for readability, efficiency: [code]"
  • Testing scaffolds: "Jest tests (normal, edge, error): [code]"

FAQ

How much token saving can I expect?

Typical savings range 50–90% depending on the original prompt; average observed reduction is ~84%.

Will minimal prompts reduce answer quality?

No, if you include required context and specify format. Minimal prompts rely on precise instructions and examples to preserve quality.