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---
name: engineering-prompts
description: Engineers effective prompts using systematic methodology. Use when designing prompts for Claude, optimizing existing prompts, or balancing simplicity, cost, and effectiveness. Applies progressive disclosure and empirical validation to prompt development.
---
# Engineering Prompts
---
## LEVEL 1: QUICKSTART ⚡
**5-Step Prompt Creation:**
1. **Start Clear**: Explicit instructions + success criteria
2. **Assess Need**: Does it need structure? Examples? Reasoning?
3. **Add Sparingly**: Only techniques that improve outcomes
4. **Estimate Cost**: Count tokens, identify caching opportunities
5. **Test & Iterate**: Measure effectiveness, refine based on results
---
## LEVEL 2: CORE PHILOSOPHY 🎯
### The Three Principles
**Simplicity First**
- Start with minimal prompt
- Add complexity only when empirically justified
- More techniques ≠ better results
**Cost Awareness**
- Minimize token usage
- Leverage prompt caching (90% savings on repeated content)
- Batch processing for non-urgent work (50% savings)
**Effectiveness**
- Techniques must improve outcomes for YOUR use case
- Measure impact, don't just apply best practices
- Iterate based on results
---
## LEVEL 3: THE 9 TECHNIQUES 🛠️
### Quick Reference
| Technique | When to Use | Cost Impact |
|-----------|------------|-------------|
| **1. Clarity** | ALWAYS | Minimal, max impact |
| **2. XML Structure** | Complex prompts, instruction leakage | ~50-100 tokens |
| **3. Chain of Thought** | Reasoning, analysis, math | 2-3x output tokens |
| **4. Multishot Examples** | Pattern learning, format guidance | 200-1K tokens each |
| **5. System Role** | Domain expertise needed | Minimal (caches well) |
| **6. Prefilling** | Strict format requirements | Minimal |
| **7. Long Context** | 20K+ token inputs | Better accuracy |
| **8. Context Budget** | Repeated use, long conversations | 90% savings with cache |
| **9. Tool Docs** | Function calling, agents | 100-500 tokens per tool |
---
## LEVEL 4: DESIGN FRAMEWORK 📋
### D - Define Requirements
**Questions to Answer:**
- Core task?
- Output format?
- Constraints (latency/cost/accuracy)?
- One-off or repeated?
### E - Estimate Complexity
**Simple:**
- Extraction, formatting
- Simple Q&A
- Clear right answer
**Medium:**
- Analysis with reasoning
- Code generation
- Multi-step but clear
**Complex:**
- Deep reasoning
- Novel problem-solving
- Research synthesis
### S - Start Simple
**Minimal Viable Prompt:**
1. Clear instruction
2. Success criteria
3. Output format
Test first. Add complexity only if underperforming.
### I - Iterate Selectively
**Add techniques based on gaps:**
- Unclear outputs → More clarity, examples
- Wrong structure → XML tags, prefilling
- Shallow reasoning → Chain of thought
- Pattern misses → Multishot examples
### G - Guide on Cost
**Cost Optimization:**
- Cache system prompts, reference docs (90% savings)
- Batch non-urgent work (50% savings)
- Minimize token usage through clear, concise instructions
### N - Note Implementation
**Deliverables:**
- The optimized prompt
- Techniques applied + rationale
- Techniques skipped + why
- Token estimate
- Caching strategy
---
## LEVEL 5: ADVANCED TOPICS 🚀
### Tool Integration
**When to use MCP tools during prompt engineering:**
```
Need latest practices?
└─ mcp__plugin_essentials_perplexity
Complex analysis needed?
└─ mcp__plugin_essentials_sequential-thinking
Need library docs?
└─ mcp__plugin_essentials_context7
```
### Context Management
**Prompt Caching:**
- Cache: System prompts, reference docs, examples
- Savings: 90% on cached content
- Write: 25% of standard cost
- Read: 10% of standard cost
**Long Context Tips:**
- Place documents BEFORE queries
- Use XML tags: `<document>`, `<source>`
- Ground responses in quotes
- 30% better performance with proper structure
### Token Optimization
**Reducing Token Usage:**
- Concise, clear instructions (no fluff)
- Reuse examples across calls (cache them)
- Structured output reduces back-and-forth
- Tool use instead of long context when possible
### Anti-Patterns
**Over-engineering** - All 9 techniques for simple task
**Premature optimization** - Complexity before testing simple
**Vague instructions** - "Analyze this" without specifics
**No examples** - Expecting format inference
**Missing structure** - Long prompts without XML
**Ignoring caching** - Not leveraging repeated content
**Stop here if:** You need advanced implementation details
---
## LEVEL 6: REFERENCES 📚
### Deep Dive Documentation
**Detailed Technique Catalog:**
- `reference/technique-catalog.md` - Each technique explained with examples, token costs, combination strategies
**Real-World Examples:**
- `reference/examples.md` - Before/after pairs for coding, analysis, extraction, agent tasks
**Research Papers:**
- `reference/research.md` - Latest Anthropic research, benchmarks, best practices evolution