senior-prompt-engineer
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
npx skills add majiayu000/claude-skill-registry --skill senior-prompt-engineer-alirezarezvani-claude-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Senior Prompt Engineer 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.
- Table of Contents
- Quick Start
- Tools Overview
- 1. Prompt Optimizer
- 2. RAG Evaluator
- 3. Agent Orchestrator
- Prompt Engineering Workflows
- Prompt Optimization Workflow
- Few-Shot Example Design Workflow
- Structured Output Design Workflow
- Reference Documentation
- Common Patterns Quick Reference
- Common Commands
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 Analyze a prompt file python scripts/prompt_optimizer.py prompt.txt --analyze Token count: 847 Estimated cost: $0.0025 (GPT-4)
What does the senior-prompt-engineer skill do?
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill senior-prompt-engineer-alirezarezvani-claude-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.