Skill UI

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.

Helps implement, audit, and validate JSON-LD schema markup so websites earn rich results, clarify content for AI search, and track structured data issues reported in Search Console before launching updates.

Refines prompt files by analyzing prior successful patterns, improving structure, wording, and clarity while preserving the original intent and markdown front matter.

This skill provides comprehensive analysis and improvement suggestions for plugin prompts, skill definitions, and command instructions. It evaluates content across five dimensions—clarity, safety, effectiveness, completeness, and conciseness—providing a detailed score card and concrete, actionable rewrites to ensure compliance and optimal AI performance.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.