prompt-engineer
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.
Best use case
prompt-engineer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using prompt-engineer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-engineer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-engineer Compares
| Feature / Agent | prompt-engineer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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 ```markdown 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 ```markdown 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)
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