This series of articles introduces design patterns for creating predictable and high-quality AI skills. The first article details five core patterns: Single Responsibility, Contract-Driven, Progressive Enhancement, Observable Design, and Defensive Output. These patterns aim to ensure that AI skills perform a single task reliably, have clearly defined inputs and outputs, handle incomplete information gracefully, provide transparency into their processes, and label uncertain information to ensure user safety. The second article focuses on evaluating AI skills, proposing a two-layer framework that assesses both trigger accuracy (whether the skill is invoked correctly) and task completion quality. It outlines metrics for trigger evaluation like recall and precision, and for task completion, it suggests structural checks and an LLM-based quality assessment across dimensions such as technical accuracy, depth, clarity, and practical value. AI
IMPACT Establishes engineering best practices for building reliable and auditable AI skills, crucial for complex agentic workflows.
RANK_REASON Articles detail methodologies and patterns for developing and evaluating AI skills, akin to software engineering best practices.
- LLM
- Skill
- competitor-analyzer
- Contract-Driven
- Defensive Output
- Observable Design
- Progressive Enhancement
- rnd-technical-writer
- Skill Design Patterns
- Skill Series
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