Skills prompt-engineering-patterns

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prompt-engineering-patterns

Name: prompt-engineering-patterns
Author: wshobson

Safe ⚙️ External commands📁 Filesystem access🌐 Network access

Master Prompt Engineering for Better AI Results

LLMs produce inconsistent results with poorly crafted prompts. This skill provides battle-tested patterns and templates for chain-of-thought reasoning, few-shot learning, and systematic prompt optimization to improve output quality.

Supports: Claude Codex Code(CC)

🥈 81 Silver

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Test it

Using "prompt-engineering-patterns". Write a prompt to summarize customer feedback

Expected outcome:

Start with the system role: You are a professional analyst.
Add specific constraints: Summarize in 3 bullet points.
Include examples: Show input-output pairs for feedback categories.
Define format: Use consistent structure for each summary.

Security Audit

Safe

v4 • 1/17/2026

This is a documentation-focused skill containing markdown guides and a local Python utility script for prompt optimization. The 228 static findings are false positives triggered by documentation patterns: backticks in Python code examples misinterpreted as shell commands, cryptographic terminology (SHA, MD5) mentioned in text, and references to API keys and file paths. The skill makes no network calls, has no sensitive filesystem access, and does not execute external commands. The optimize-prompt.py script uses a mock LLM client for local testing only.

Files scanned

2,919

Lines analyzed

findings

Total audits

Risk Factors

⚙️ External commands (169)

📁 Filesystem access (3)

references/prompt-optimization.md:128 references/prompt-optimization.md:128 scripts/optimize-prompt.py:229

🌐 Network access (1)

skill-report.json:6

Audited by: claude View Audit History →

Quality Score

Architecture

100

Maintainability

Content

Community

100

Security

Spec Compliance

What You Can Build

Optimize Production Prompts

Systematically test and refine prompts for production LLM applications using A/B testing frameworks.

Build Template Libraries

Create reusable prompt templates with variable interpolation for consistent content generation.

Apply Advanced Techniques

Implement chain-of-thought and self-consistency patterns for complex reasoning tasks.

Try These Prompts

Simple Classification

Classify this text into one of these categories: Positive, Negative, Neutral.

Text: {text}

Category:

Few-Shot Extraction

Extract information in JSON format.

Example:
Text: Apple CEO Tim Cook announced new iPhone.
Output: {"persons":["Tim Cook"],"organizations":["Apple"],"products":["iPhone"]}

Text: {text}

Output:

Chain-of-Thought

Solve this step by step.

Problem: {problem}

Step 1: Identify what we know
Step 2: Determine the approach
Step 3: Calculate
Step 4: Verify

Answer:

Self-Consistency

Solve this problem three different ways. Then identify which answer appears most frequently.

Problem: {problem}

Approach 1:
Result:

Approach 2:
Result:

Approach 3:
Result:

Final Answer (most common):

Best Practices

Be specific about format, length, and style requirements rather than relying on implied instructions
Use few-shot examples to demonstrate the exact output format you need, especially for structured data
Test prompts on edge cases and diverse inputs before deploying to production

Avoid

Overloading prompts with too many examples, causing token limits to reduce space for actual input
Using vague instructions like 'be helpful' or 'be accurate' that different models interpret differently
Skipping verification steps for factual or logical outputs that require validation

Frequently Asked Questions

Which LLMs work with these patterns?

Patterns work with Claude, GPT-4, Claude Code, and most instruction-tuned models. Some techniques like chain-of-thought work best on reasoning-capable models.

What is the optimal number of few-shot examples?

Most tasks perform well with 3 to 5 examples. More examples can dilute focus and consume token budget. Test different counts for your specific use case.

How do I integrate with my existing codebase?

The skill provides template systems and Python utilities. Adapt the PromptTemplate classes to your LLM client. The optimize-prompt.py script shows a testing workflow.

Is my data sent anywhere?

No. This skill runs locally. The reference materials and utility scripts operate entirely on your machine. No external network calls are made by any component.

Why do my prompts work differently across models?

Models have different training and capabilities. Test and adjust templates per model. Chain-of-thought works better on reasoning models. Some models need more explicit format instructions.

How does this compare to other prompting skills?

This skill focuses on production-ready patterns with systematic optimization workflows. It covers template systems, A/B testing, and evaluation metrics for real-world deployment.