senior-prompt-engineer
Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.
- Installs
- 230
- Last 24h
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/borghei/claude-skills --skill senior-prompt-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads senior-prompt-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Senior Prompt Engineer
Prompt engineering patterns, LLM evaluation frameworks, and agentic system design. Provides static (deterministic) analysis tools to optimize prompts, evaluate RAG retrieval and generation quality, and validate/visualize agent workflows — plus deep reference libraries of prompt patterns, evaluation metrics, and agent architectures.
Core Capabilities
- Prompt optimization — token counting and cost estimation, clarity/structure scoring, ambiguity and redundancy detection, and generation of optimized prompt versions.
- Few-shot & structured output design — extract/manage few-shot examples, design diverse example sets (simple/edge/complex/negative), and enforce reliable JSON/XML schema outputs.
- RAG evaluation — context relevance, answer faithfulness, groundedness (ROUGE-L), and retrieval metrics (Precision@K, MRR, NDCG) over pre-retrieved contexts.
- Agentic system design — validate agent configs, visualize flows (ASCII/Mermaid), estimate token cost per run, and apply ReAct / Plan-Execute / Tool-Use / multi-agent patterns.
- Pattern library — 10 prompt patterns, evaluation frameworks (A/B testing, benchmarks, human eval), and agent architectures with pseudocode.
When to Use
- Optimizing an existing prompt's performance or reducing token costs.
- Designing prompt templates, few-shot examples, or structured-output workflows.
- Evaluating LLM outputs or RAG retrieval/generation quality.
- Building or validating agentic systems and tool-calling workflows.
Tools
| Tool | Purpose | Command |
|---|---|---|
prompt_optimizer.py | Analyze/optimize prompts: tokens, clarity, structure, few-shot extraction | python scripts/prompt_optimizer.py prompt.txt --analyze |
rag_evaluator.py | Evaluate RAG context relevance, faithfulness, retrieval metrics | python scripts/rag_evaluator.py --contexts ctx.json --questions q.json |
agent_orchestrator.py | Validate, visualize, and cost-estimate agent configs | python scripts/agent_orchestrator.py agent.yaml --validate |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/tools-and-workflows.md — full tool usage with sample outputs, the prompt-optimization / few-shot / structured-output workflows, common-patterns and command quick references, troubleshooting table, success criteria, and complete per-script parameter/output-format reference. Read when running any tool or executing a workflow.
- references/prompt_engineering_patterns.md — 10 prompt patterns (zero/few-shot, CoT, role, structured output, self-consistency, ReAct, tree-of-thoughts, RAG) with example inputs and expected outputs. Read when choosing or applying a prompt technique.
- references/llm_evaluation_frameworks.md — evaluation metrics, text-generation and RAG-specific scoring, human-eval frameworks, A/B testing, benchmark datasets, and pipeline design. Read when measuring quality or comparing prompts.
- references/agentic_system_design.md — agent architectures (ReAct, Plan-and-Execute, Tool Use, multi-agent, memory/state) and design patterns with pseudocode. Read when building agents or tool-calling systems.
Scope & Limitations
This skill covers:
- Static prompt analysis: token counting, clarity scoring, structure detection, and optimization suggestions
- RAG evaluation: context relevance, answer faithfulness, groundedness, and retrieval metrics (Precision@K, ROUGE-L, MRR, NDCG)
- Agent workflow design: configuration validation, ASCII/Mermaid visualization, and token cost estimation
- Few-shot example extraction and management from existing prompts
This skill does NOT cover:
- Live LLM calls or runtime prompt testing --- all analysis is static/deterministic (see
senior-ml-engineerfor LLM integration) - Vector database setup or embedding generation --- RAG evaluator scores pre-retrieved contexts only (see
senior-data-engineerfor pipeline orchestration) - Fine-tuning, RLHF, or model training workflows (see
senior-ml-engineerfor model deployment) - Production monitoring, A/B test execution, or real-time drift detection (see
senior-data-scientistfor experiment design)
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
senior-ml-engineer | LLM integration and model deployment | Optimized prompts from this skill feed into the prompt templates of its LLM integration layer |
senior-data-scientist | A/B test design for prompt experiments | experiment_designer.py defines test parameters; this skill provides the prompt variants to compare |
senior-data-engineer | RAG pipeline orchestration | pipeline_orchestrator.py builds the retrieval pipeline; this skill evaluates its output quality |
senior-fullstack | End-to-end application scaffolding | Fullstack apps consume agent configs validated by agent_orchestrator.py |
senior-security | Prompt injection and adversarial input review | Security analysis covers the attack surface; this skill ensures prompts include defensive constraints |
senior-qa | Quality assurance for AI-powered features | QA test suites validate that optimized prompts produce consistent outputs in production |
FAQ
What does the senior-prompt-engineer skill do?
Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.
How do I install senior-prompt-engineer?
Run npx skills add https://github.com/borghei/claude-skills --skill senior-prompt-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/senior-prompt-engineer/ (personal) or .claude/skills/senior-prompt-engineer/ (project).
Which AI agents can use senior-prompt-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is senior-prompt-engineer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains senior-prompt-engineer?
It is published by borghei in borghei/claude-skills. It has 230 recorded installs.
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