Public skill
senior-prompt-engineer
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
Package contents
Manifest
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
db48da952752ad05d4adeb2140a7a31d2d6b70c98a81f73f996ccd6e1bb3d6d8- SKILL.md9,954 bytes
- references/agentic_system_design.md21,700 bytes
- references/llm_evaluation_frameworks.md13,303 bytes
- references/prompt_engineering_patterns.md13,675 bytes
- scripts/agent_orchestrator.py21,450 bytes
- scripts/prompt_optimizer.py19,039 bytes
- scripts/rag_evaluator.py20,362 bytes
Release history