Test and refine a Claude Agent Skill: a walkthrough
A concrete walkthrough: scaffold, build an eval set, run it with samples, read trigger vs quality scores, and refine a Claude Agent Skill with skill-creator.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
A concrete walkthrough: scaffold, build an eval set, run it with samples, read trigger vs quality scores, and refine a Claude Agent Skill with skill-creator.
How skill-creator works inside: discovery, the eval harness, trigger vs execution scoring, variance analysis, and the refine loop for Claude Agent Skills.
Comparing Google's Agent-to-Agent (A2A) protocol with Anthropic's Model Context Protocol (MCP), explaining how each approach solves agent interoperability differently.
How to build production-grade data pipelines that use LLMs to extract structured data from unstructured sources with validation, error handling, and quality monitoring.
Turing benchmarks 6 AI agent frameworks across 2000 test runs measuring latency, token efficiency, and task completion rates for production use.
Turing company agentic solutions: turing benchmarks 6 AI agent frameworks across 2000 test runs measuring latency, token efficiency, and task completion. Detailed comparison results.
Learn how agentic AI systems automate ESG reporting, carbon footprint tracking, and sustainability compliance across global regulatory frameworks.
Massive Multitask Language Understanding (MMLU) benchmark evaluates general knowledge and reasoning
AI agents now complete whole college courses autonomously. What this means for enterprise training, workforce development, and L&D strategy.