senior-prompt-engineer
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
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Installation for Agentic Skill
View all platforms →skilz install alirezarezvani/claude-skills/senior-prompt-engineerskilz install alirezarezvani/claude-skills/senior-prompt-engineer --agent opencodeskilz install alirezarezvani/claude-skills/senior-prompt-engineer --agent codexskilz install alirezarezvani/claude-skills/senior-prompt-engineer --agent geminiFirst time? Install Skilz: pip install skilz
Works with 22+ AI coding assistants
Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...
Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/alirezarezvani/claude-skillscp -r claude-skills/engineering-team/senior-prompt-engineer ~/.claude/skills/Need detailed installation help? Check our platform-specific guides:
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Agentic Skill Details
- Owner
- alirezarezvani (GitHub)
- Repository
- claude-skills
- Stars
- 579
- Forks
- 112
- Type
- Other
- Meta-Domain
- Primary Domain
- Market Score
- 0
Agent Skill Grade
F Score: 36/100 Click to see breakdown
Score Breakdown
Areas to Improve
- Marketing Language Overuse
- Duplicate Reference Content
- Missing TOC for Long File
Recommendations
- Focus on improving Pda (currently 8/30)
- Focus on improving Ease Of Use (currently 9/25)
- Focus on improving Writing Style (currently 2/10)
Graded: 2026-01-24
Developer Feedback
I looked at your senior-prompt-engineer skill and noticed the grading rubric heavily penalizes the documentation structure—the core ideas are solid, but they're buried under formatting choices that make them harder to follow for developers actually trying to use the skill.
Links:
The TL;DR
You're at 36/100, which is an F grade. This is based on Anthropic's best practices for skill design. Your strongest area is Spec Compliance (12/15)—the YAML structure and naming conventions are solid. The real drag is Utility (4/20) and Writing Style (2/10)—you've got aspirational concepts but they lack concrete, actionable workflows that developers can actually execute.
What's Working Well
- Valid metadata structure - Your YAML frontmatter is clean and follows conventions perfectly
- Decent trigger coverage - You've identified relevant use cases like "optimizing LLM performance" and "designing agentic systems"
- Reference architecture - Having three reference files shows you're thinking layered, even if the content needs work
- Grep-friendly structure - The skill is organized in a way that's easy to search and navigate
The Big One: Duplicate Reference Content
Here's what's killing your score: all three reference files (prompt_engineering_patterns.md, agentic_system_design.md, llm_evaluation_frameworks.md) are 80%+ identical boilerplate. They share the same "Core Principles," "Advanced Patterns," and "Best Practices" sections—which defeats the whole purpose of having multiple references.
Why it matters: Progressive Disclosure Architecture is supposed to layer specific, relevant details. Right now you're wasting tokens repeating the same content three time...
AI-Detected Topics
Extracted using NLP analysis
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