prompt-engineering-patterns

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# Prompt Engineering Patterns V1.0

> **Purpose:** Pre-process and optimize user requests before orchestrator execution.
> **Trigger:** Vague, ambiguous, or incomplete requests that need expansion.

---

## WHEN TO USE THIS SKILL

- Designing complex prompts for production LLM applications
- Optimizing prompt performance and consistency
- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
- Building few-shot learning systems with dynamic example selection
- Creating reusable prompt templates with variable interpolation
- Debugging and refining prompts that produce inconsistent outputs
- Implementing system prompts for specialized AI assistants
- Using structured outputs (JSON mode) for reliable parsing
- **Pre-processing vague user requests for orchestrator**

---

## PRE-PROCESSING MODE

When invoked by the orchestrator for request optimization:

### STEP 1: ANALYZE REQUEST COMPLEXITY

| Criteria | Simple (Skip) | Complex (Process) |
|----------|---------------|-------------------|
| Word count | > 15 words | < 10 words |
| Specificity | Has file paths, technical details | Vague terms only |
| Ambiguity | Clear intent | "fix", "improve", "optimize" |
| Scope | Single task | Multiple tasks ("and", "also") |

### STEP 2: IDENTIFY TASK TYPE

```markdown
| Type | Keywords | Routing |
|------|----------|---------|
| BUG_FIX | fix, error, bug, broken, crash | Coder → Tester |
| FEATURE | add, create, implement, new | Architect → Coder |
| REFACTOR | refactor, clean, restructure | Languages Refactor L2 |
| ANALYSIS | analyze, review, check, audit | Analyzer |
| DEBUG | debug, investigate, why | Tester Expert |
| TEST | test, coverage, pytest | Test Unit L2 |
| SECURITY | security, vulnerability, auth | Security Expert |
| PERFORMANCE | optimize, faster, slow | Architect Expert |
| INTEGRATION | integrate, connect, API | Integration Expert |
```

### STEP 3: EXPAND VAGUE REQUESTS

```markdown
| Vague Term | Expansion |
|------------|-----------|
| "fix" | Identify root cause → Implement fix → Add tests → Verify |
| "improve" | Analyze current → Identify improvements → Prioritize → Implement |
| "optimize" | Profile → Find bottleneck → Optimize → Benchmark |
| "make better" | Define metrics → Identify issues → Implement → Measure |
| "clean up" | Identify smells → Apply clean code → Document |
```

### STEP 4: OUTPUT OPTIMIZED REQUEST

```markdown
## OPTIMIZED REQUEST

### Original
[Original user request verbatim]

### Task Type
[BUG_FIX | FEATURE | REFACTOR | ANALYSIS | DEBUG | TEST | SECURITY | PERFORMANCE | INTEGRATION]

### Expanded Request
[Detailed, specific request with all necessary information]

### Suggested Approach
1. [First step]
2. [Second step]
...

### Files Likely Involved
- [file path 1]
- [file path 2]

### Success Criteria
- [Criterion 1]
- [Criterion 2]
```

---

## CORE CAPABILITIES

### 1. Few-Shot Learning

- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
- Constructing effective demonstrations with input-output pairs
- Dynamic example retrieval from knowledge bases

### 2. Chain-of-Thought Prompting

- Step-by-step reasoning elicitation
- Zero-shot CoT with "Let's think step by step"
- Few-shot CoT with reasoning traces
- Self-consistency techniques (sampling multiple reasoning paths)

### 3. Structured Outputs

- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs

### 4. Prompt Optimization

- Iterative refinement workflows
- A/B testing prompt variations

From eroslifestyle/Claude-Orchestrator-Plugin, MIT.

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