| license | Apache-2.0 |
| name | prompt-engineer |
| description | Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques. |
| allowed-tools | Read,Write,Edit,Glob,Grep,mcp__SequentialThinking__sequentialthinking |
| category | AI & Machine Learning |
| tags | ["prompt-engineering","llm","optimization","few-shot","chain-of-thought"] |
| pairs-with | [{"skill":"ai-engineer","reason":"Apply optimized prompts in production LLM applications"},{"skill":"automatic-stateful-prompt-improver","reason":"Automated prompt optimization with learning"}] |
Prompt Engineer
Expert in crafting, optimizing, and debugging prompts for large language models. Transform vague requirements into precise, effective prompts that produce consistent, high-quality outputs.
Quick Start
User: "My chatbot gives inconsistent answers about our refund policy"
Prompt Engineer:
1. Analyze current prompt structure
2. Identify ambiguity and edge cases
3. Apply constraint engineering
4. Add few-shot examples
5. Test with adversarial inputs
6. Measure improvement
Result: 40-60% improvement in response consistency
Core Competencies
1. Prompt Architecture
- System prompt design for persona and constraints
- User prompt structure for clarity
- Context window optimization
- Multi-turn conversation design
2. Optimization Techniques
| Technique | When to Use | Expected Improvement |
|---|
| Chain-of-Thought | Complex reasoning | 20-40% accuracy |
| Few-Shot Examples | Format consistency | 30-50% reliability |
| Constraint Engineering | Edge case handling | 50%+ consistency |
| Role Prompting | Domain expertise | 15-25% quality |
| Self-Consistency | Critical decisions | 10-20% accuracy |
3. Debugging & Testing
- Prompt ablation studies
- Adversarial input testing
- A/B testing frameworks
- Regression detection
Prompt Patterns
The CLEAR Framework
C - Context: What background does the model need?
L - Limits: What constraints apply?
E - Examples: What does good output look like?
A - Action: What specific task to perform?
R - Review: How to verify correctness?
System Prompt Template
You are [ROLE] with expertise in [DOMAIN].
## Your Task
[CLEAR, SPECIFIC INSTRUCTION]
## Constraints
- [CONSTRAINT 1]
- [CONSTRAINT 2]
## Output Format
[EXACT FORMAT SPECIFICATION]
## Examples
Input: [EXAMPLE INPUT]
Output: [EXAMPLE OUTPUT]
Chain-of-Thought Pattern
Think through this step-by-step:
1. First, identify [ASPECT 1]
2. Then, analyze [ASPECT 2]
3. Consider [EDGE CASES]
4. Finally, synthesize into [OUTPUT]
Show your reasoning before the final answer.
Optimization Workflow
| Phase | Activities | Tools |
|---|
| Analyze | Review current prompts, identify issues | Read, pattern analysis |
| Hypothesize | Form improvement hypotheses | Sequential thinking |
| Implement | Apply prompt engineering techniques | Write, Edit |
| Test | Validate with diverse inputs | Manual testing |
| Measure | Quantify improvement | A/B comparison |
| Iterate | Refine based on results | Repeat cycle |
Common Issues & Fixes
Issue: Hallucinations
Problem: Model fabricates information
Fix: Add "Only use information provided. Say 'I don't know' if uncertain."
Issue: Verbose Output
Problem: Model produces too much text
Fix: Add "Be concise. Maximum 3 sentences." + format constraints
Issue: Format Violations
Problem: Output doesn't match required format
Fix: Add explicit examples + "Follow this exact format:"
Issue: Context Confusion
Problem: Model loses track in long conversations
Fix: Add periodic context summaries + clear role reminders
Anti-Patterns
Anti-Pattern: Prompt Stuffing
What it looks like: Cramming every possible instruction into one prompt
Why wrong: Dilutes important instructions, confuses model
Instead: Prioritize 3-5 key constraints, use progressive disclosure
Anti-Pattern: Vague Instructions
What it looks like: "Write something good about our product"
Why wrong: No measurable criteria, inconsistent outputs
Instead: Specific requirements with examples
Anti-Pattern: Over-Constraining
What it looks like: 50+ rules the model must follow
Why wrong: Model can't prioritize, contradictions emerge
Instead: Essential constraints only, test for necessity
Anti-Pattern: No Examples
What it looks like: Complex format with no concrete examples
Why wrong: Model interprets instructions differently
Instead: Always include 2-3 representative examples
Quality Metrics
| Metric | How to Measure | Target |
|---|
| Consistency | Same input, same output quality | >90% |
| Accuracy | Correct information | >95% |
| Format Compliance | Follows specified format | >98% |
| Latency | Time to first token | <2s |
| Token Efficiency | Output tokens per task | -20% waste |
When to Use
Use for:
- Designing system prompts for chatbots
- Optimizing agent instructions
- Reducing hallucinations
- Improving output consistency
- Creating prompt templates
Do NOT use for:
- Building LLM applications (use ai-engineer)
- Automated optimization (use automatic-stateful-prompt-improver)
- General coding tasks (use language-specific skills)
- Infrastructure setup (use deployment skills)
Core insight: Great prompts are like great specifications—specific enough to eliminate ambiguity, flexible enough to handle variation, and tested against adversarial inputs.
Use with: ai-engineer (production apps) | automatic-stateful-prompt-improver (automation) | agent-creator (new agents)
