Agent Skills: Senior Prompt Engineer

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-engineer for LLM integration)
• Vector database setup or embedding generation --- RAG evaluator scores pre-retrieved contexts only (see senior-data-engineer for pipeline orchestration)
• Fine-tuning, RLHF, or model training workflows (see senior-ml-engineer for model deployment)
• Production monitoring, A/B test execution, or real-time drift detection (see senior-data-scientist for experiment design)

Integration Points

| Skill | Integration | Data Flow | |-------|-------------|-----------| | senior-ml-engineer | LLM integration and model deployment | Optimized prompts from this skill feed into llm_integration_builder.py prompt templates | | 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 |