---
title: "Senior Prompt Engineer"
description: "Senior Prompt Engineer - Claude Code skill from the Engineering - Core domain."
---

# Senior Prompt Engineer

<div class="page-meta" markdown>
<span class="meta-badge">:material-code-braces: Engineering - Core</span>
<span class="meta-badge">:material-identifier: `senior-prompt-engineer`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/senior-prompt-engineer/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install engineering-skills</code>
</div>


Prompt engineering patterns, LLM evaluation frameworks, and agentic system design.

## Table of Contents

- [Quick Start](#quick-start)
- [Tools Overview](#tools-overview)
  - [Prompt Optimizer](#1-prompt-optimizer)
  - [RAG Evaluator](#2-rag-evaluator)
  - [Agent Orchestrator](#3-agent-orchestrator)
- [Prompt Engineering Workflows](#prompt-engineering-workflows)
  - [Prompt Optimization Workflow](#prompt-optimization-workflow)
  - [Few-Shot Example Design](#few-shot-example-design-workflow)
  - [Structured Output Design](#structured-output-design-workflow)
- [Reference Documentation](#reference-documentation)
- [Common Patterns Quick Reference](#common-patterns-quick-reference)

---

## Quick Start

```bash
# Analyze and optimize a prompt file
python scripts/prompt_optimizer.py prompts/my_prompt.txt --analyze

# Evaluate RAG retrieval quality
python scripts/rag_evaluator.py --contexts contexts.json --questions questions.json

# Visualize agent workflow from definition
python scripts/agent_orchestrator.py agent_config.yaml --visualize
```

---

## Tools Overview

### 1. Prompt Optimizer

Analyzes prompts for token efficiency, clarity, and structure. Generates optimized versions.

**Input:** Prompt text file or string
**Output:** Analysis report with optimization suggestions

**Usage:**
```bash
# Analyze a prompt file
python scripts/prompt_optimizer.py prompt.txt --analyze

# Output:
# Token count: 847
# Estimated cost: $0.0025 (GPT-4)
# Clarity score: 72/100
# Issues found:
#   - Ambiguous instruction at line 3
#   - Missing output format specification
#   - Redundant context (lines 12-15 repeat lines 5-8)
# Suggestions:
#   1. Add explicit output format: "Respond in JSON with keys: ..."
#   2. Remove redundant context to save 89 tokens
#   3. Clarify "analyze" -> "list the top 3 issues with severity ratings"

# Generate optimized version
python scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt

# Count tokens for cost estimation
python scripts/prompt_optimizer.py prompt.txt --tokens --model gpt-4

# Extract and manage few-shot examples
python scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json
```

---

### 2. RAG Evaluator

Evaluates Retrieval-Augmented Generation quality by measuring context relevance and answer faithfulness.

**Input:** Retrieved contexts (JSON) and questions/answers
**Output:** Evaluation metrics and quality report

**Usage:**
```bash
# Evaluate retrieval quality
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json

# Output:
# === RAG Evaluation Report ===
# Questions evaluated: 50
#
# Retrieval Metrics:
#   Context Relevance: 0.78 (target: >0.80)
#   Retrieval Precision@5: 0.72
#   Coverage: 0.85
#
# Generation Metrics:
#   Answer Faithfulness: 0.91
#   Groundedness: 0.88
#
# Issues Found:
#   - 8 questions had no relevant context in top-5
#   - 3 answers contained information not in context
#
# Recommendations:
#   1. Improve chunking strategy for technical documents
#   2. Add metadata filtering for date-sensitive queries

# Evaluate with custom metrics
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json \
    --metrics relevance,faithfulness,coverage

# Export detailed results
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json \
    --output report.json --verbose
```

---

### 3. Agent Orchestrator

Parses agent definitions and visualizes execution flows. Validates tool configurations.

**Input:** Agent configuration (YAML/JSON)
**Output:** Workflow visualization, validation report

**Usage:**
```bash
# Validate agent configuration
python scripts/agent_orchestrator.py agent.yaml --validate

# Output:
# === Agent Validation Report ===
# Agent: research_assistant
# Pattern: ReAct
#
# Tools (4 registered):
#   [OK] web_search - API key configured
#   [OK] calculator - No config needed
#   [WARN] file_reader - Missing allowed_paths
#   [OK] summarizer - Prompt template valid
#
# Flow Analysis:
#   Max depth: 5 iterations
#   Estimated tokens/run: 2,400-4,800
#   Potential infinite loop: No
#
# Recommendations:
#   1. Add allowed_paths to file_reader for security
#   2. Consider adding early exit condition for simple queries

# Visualize agent workflow (ASCII)
python scripts/agent_orchestrator.py agent.yaml --visualize

# Output:
# ┌─────────────────────────────────────────┐
# │            research_assistant           │
# │              (ReAct Pattern)            │
# └─────────────────┬───────────────────────┘
#                   │
#          ┌────────▼────────┐
#          │   User Query    │
#          └────────┬────────┘
#                   │
#          ┌────────▼────────┐
#          │     Think       │◄──────┐
#          └────────┬────────┘       │
#                   │                │
#          ┌────────▼────────┐       │
#          │   Select Tool   │       │
#          └────────┬────────┘       │
#                   │                │
#     ┌─────────────┼─────────────┐  │
#     ▼             ▼             ▼  │
# [web_search] [calculator] [file_reader]
#     │             │             │  │
#     └─────────────┼─────────────┘  │
#                   │                │
#          ┌────────▼────────┐       │
#          │    Observe      │───────┘
#          └────────┬────────┘
#                   │
#          ┌────────▼────────┐
#          │  Final Answer   │
#          └─────────────────┘

# Export workflow as Mermaid diagram
python scripts/agent_orchestrator.py agent.yaml --visualize --format mermaid
```

---

## Prompt Engineering Workflows

### Prompt Optimization Workflow

Use when improving an existing prompt's performance or reducing token costs.

**Step 1: Baseline current prompt**
```bash
python scripts/prompt_optimizer.py current_prompt.txt --analyze --output baseline.json
```

**Step 2: Identify issues**
Review the analysis report for:
- Token waste (redundant instructions, verbose examples)
- Ambiguous instructions (unclear output format, vague verbs)
- Missing constraints (no length limits, no format specification)

**Step 3: Apply optimization patterns**
| Issue | Pattern to Apply |
|-------|------------------|
| Ambiguous output | Add explicit format specification |
| Too verbose | Extract to few-shot examples |
| Inconsistent results | Add role/persona framing |
| Missing edge cases | Add constraint boundaries |

**Step 4: Generate optimized version**
```bash
python scripts/prompt_optimizer.py current_prompt.txt --optimize --output optimized.txt
```

**Step 5: Compare results**
```bash
python scripts/prompt_optimizer.py optimized.txt --analyze --compare baseline.json
# Shows: token reduction, clarity improvement, issues resolved
```

**Step 6: Validate with test cases**
Run both prompts against your evaluation set and compare outputs.

---

### Few-Shot Example Design Workflow

Use when creating examples for in-context learning.

**Step 1: Define the task clearly**
```
Task: Extract product entities from customer reviews
Input: Review text
Output: JSON with {product_name, sentiment, features_mentioned}
```

**Step 2: Select diverse examples (3-5 recommended)**
| Example Type | Purpose |
|--------------|---------|
| Simple case | Shows basic pattern |
| Edge case | Handles ambiguity |
| Complex case | Multiple entities |
| Negative case | What NOT to extract |

**Step 3: Format consistently**
```
Example 1:
Input: "Love my new iPhone 15, the camera is amazing!"
Output: {"product_name": "iPhone 15", "sentiment": "positive", "features_mentioned": ["camera"]}

Example 2:
Input: "The laptop was okay but battery life is terrible."
Output: {"product_name": "laptop", "sentiment": "mixed", "features_mentioned": ["battery life"]}
```

**Step 4: Validate example quality**
```bash
python scripts/prompt_optimizer.py prompt_with_examples.txt --validate-examples
# Checks: consistency, coverage, format alignment
```

**Step 5: Test with held-out cases**
Ensure model generalizes beyond your examples.

---

### Structured Output Design Workflow

Use when you need reliable JSON/XML/structured responses.

**Step 1: Define schema**
```json
{
  "type": "object",
  "properties": {
    "summary": {"type": "string", "maxLength": 200},
    "sentiment": {"enum": ["positive", "negative", "neutral"]},
    "confidence": {"type": "number", "minimum": 0, "maximum": 1}
  },
  "required": ["summary", "sentiment"]
}
```

**Step 2: Include schema in prompt**
```
Respond with JSON matching this schema:
- summary (string, max 200 chars): Brief summary of the content
- sentiment (enum): One of "positive", "negative", "neutral"
- confidence (number 0-1): Your confidence in the sentiment
```

**Step 3: Add format enforcement**
```
IMPORTANT: Respond ONLY with valid JSON. No markdown, no explanation.
Start your response with { and end with }
```

**Step 4: Validate outputs**
```bash
python scripts/prompt_optimizer.py structured_prompt.txt --validate-schema schema.json
```

---

## Reference Documentation

| File | Contains | Load when user asks about |
|------|----------|---------------------------|
| `references/prompt_engineering_patterns.md` | 10 prompt patterns with input/output examples | "which pattern?", "few-shot", "chain-of-thought", "role prompting" |
| `references/llm_evaluation_frameworks.md` | Evaluation metrics, scoring methods, A/B testing | "how to evaluate?", "measure quality", "compare prompts" |
| `references/agentic_system_design.md` | Agent architectures (ReAct, Plan-Execute, Tool Use) | "build agent", "tool calling", "multi-agent" |

---

## Common Patterns Quick Reference

| Pattern | When to Use | Example |
|---------|-------------|---------|
| **Zero-shot** | Simple, well-defined tasks | "Classify this email as spam or not spam" |
| **Few-shot** | Complex tasks, consistent format needed | Provide 3-5 examples before the task |
| **Chain-of-Thought** | Reasoning, math, multi-step logic | "Think step by step..." |
| **Role Prompting** | Expertise needed, specific perspective | "You are an expert tax accountant..." |
| **Structured Output** | Need parseable JSON/XML | Include schema + format enforcement |

---

## Common Commands

```bash
# Prompt Analysis
python scripts/prompt_optimizer.py prompt.txt --analyze          # Full analysis
python scripts/prompt_optimizer.py prompt.txt --tokens           # Token count only
python scripts/prompt_optimizer.py prompt.txt --optimize         # Generate optimized version

# RAG Evaluation
python scripts/rag_evaluator.py --contexts ctx.json --questions q.json  # Evaluate
python scripts/rag_evaluator.py --contexts ctx.json --compare baseline  # Compare to baseline

# Agent Development
python scripts/agent_orchestrator.py agent.yaml --validate       # Validate config
python scripts/agent_orchestrator.py agent.yaml --visualize      # Show workflow
python scripts/agent_orchestrator.py agent.yaml --estimate-cost  # Token estimation
```
