senior-prompt-engineer

name	senior-prompt-engineer
description	"Use — This skill should be used when the user asks to 'optimize prompts', 'design prompt templates', 'evaluate LLM"

Senior Prompt Engineer

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

Table of Contents

• Quick Start
• Tools Overview
  • Prompt Optimizer
  • RAG Evaluator
  • Agent Orchestrator
• Prompt Engineering Workflows
  • Prompt Optimization Workflow
  • Few-Shot Example Design
  • Structured Output Design
• Reference Documentation
• Common Patterns Quick Reference

---

Quick Start

# 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:

# 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:

# 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:

# 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   │       │
#          └────────┬────────┘       │
#                   │                │















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

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

python scripts/prompt_optimizer.py current_prompt.txt --optimize --output optimized.txt

Step 5: Compare results

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

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

{
  "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

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

# 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

Diff History

• v00.33.0: Ingested from claude-skills-main

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Why This Skill Exists

Use — This skill should be used when the user asks to

When to Use

Use this skill when the task requires senior prompt engineer capabilities.

What If Fails

• condition: Modelo de ML indisponível ou não carregado