prompt-engineering
Prompt templates, few-shot examples, chain-of-thought, structured output, evals
Profile →npx skills add vibeeval/vibecosystem --skill prompt-engineering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Prompt Engineering ## Prompt Templates ### System Prompt Structure ```python SYSTEM_PROMPT = """You are a {role} specialized in {domain}. ## Task {task_description} ## Rules {numbered_rules} ## Output Format {format_spec} ## Examples {few_shot_examples} """ ``` ### Few-Shot Template ```python def build_few_shot_prompt(task: str, examples: list[dict], query: str) -> str: prompt = f"Task: {task}\n\n" for i, ex in enumerate(examples, 1): prompt += f"Example {i}:\nInput: {ex['input']}\nOutput: {ex['output']}\n\n" prompt += f"Now process:\nInput: {query}\nOutput:" return prompt # Usage examples = [ {"input": "The food was great", "output": '{"sentiment": "positive", "confidence": 0.95}'}, {"input": "Terrible service", "output": '{"sentiment": "negative", "confidence": 0.90}'}, {"input": "It was okay", "output": '{"sentiment": "neutral", "confidence": 0.70}'}, ] prompt = build_few_shot_prompt("Classify sentiment as JSON", examples, "Really loved it!") ``` ## Chain-of-Thought Patterns ### Explicit CoT ``` Analyze this code for security vulnerabilities. Think step by step: 1. Identify all user inputs 2. Trace each input through the code 3. Check if any input reaches a sensitive operation
- Prompt Templates
- System Prompt Structure
- Few-Shot Template
- Chain-of-Thought Patterns
- Explicit CoT
- Self-Consistency (Multiple CoT paths)
- Structured Output
- JSON Mode with Schema
- XML Tags for Claude
- Prompt Evaluation Framework
- Checklist
- Anti-Patterns
What does the prompt-engineering skill do?
Prompt templates, few-shot examples, chain-of-thought, structured output, evals
How do I install it?
Run `npx skills add vibeeval/vibecosystem --skill prompt-engineering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From vibeeval/vibecosystem, a repository with 521 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.