AI Coding Summit London

What makes an agent skill reliable, performant, and maintainable? We will explore a robust approach to skill design, starting with foundational best practices, moving into automated skill generation, and validation. The second half of the talk focuses on the critical role of evaluation, demonstrating how tools like SkillGrade and benchmarks like SkillBench allow developers to catch regressions and ensure their agents behave predictably in complex environments.

This talk dives into how developers can build structured, repeatable coding workflows using Claude Code's ecosystem: Skills, Subagents, settings, Hooks, and MCP servers. I'll walk through the architecture behind Claude Code Templates, an open-source project with 120K+ npm downloads and 23K+ GitHub stars, showing how these components work together to create composable, reusable patterns within real software development projects. From automating code reviews to orchestrating multi-agent tasks, we'll cover practical setups that teams can adopt immediately.

AI-generated code is becoming the norm, but who reviews the reviewer? In this session, we explore how to close the feedback loop by letting AI agents review AI-written code. We'll look at local agent setups as well as cloud-based services like GitHub Copilot code review or Greptile, and discuss when each approach makes sense. Walk away with a practical mental model for building a self-correcting AI development workflow, without losing control over your codebase.