ai-engineering-toolkitlisted

# AI Engineering Toolkit ## Overview A collection of 6 structured, expert-level workflows that turn your AI coding assistant into a senior AI engineering partner. Each skill encodes a repeatable methodology — not just "ask AI to help," but a step-by-step decision framework with quantitative scoring, checklists, and decision trees. The key difference from ad-hoc AI assistance: **every workflow produces consistent, reproducible results** regardless of who runs it or when. You can use the scoring systems as team baselines and write them into CI/CD pipelines. ## When to Use This Skill - Use when evaluating or optimizing LLM system prompts before production deployment - Use when designing a RAG pipeline and need structured architecture decisions (not just boilerplate code) - Use when planning token budget allocation across context window zones - Use when running pre-launch security audits on AI agents - Use when building evaluation frameworks for LLM applications - Use when thinking through product strategy before writing code ## How It Works ### Skill 1: Prompt Evaluator Scores prompts across 8 dimensions (Clarity, Specificity, Completeness, Conciseness, Structure, Grounding, Safety, Robustness) on a 1-10 scale with weighted aggregation to a 0-100 score. Identifies the 3 weakest dimensions, generates targeted rewrites, and re-evaluates. Supports single prompt, A/B comparison, and batch evaluation modes. ### Skill 2: Context Budget Planner Analyzes token distribution acr