Hamel Husain Releases Open-Source Eval Skills for AI Coding Agents
@HamelHusain
Teams deploying AI agents without a rigorous evaluation framework have no reliable signal on whether their systems are improving or degrading — this open-source resource gives leaders a proven, structured starting point to close that gap.
Hamel Husain Releases Open-Source Eval Skills for AI Coding Agents
Hamel Husain has published Evals Skills for Coding Agents, an open-source resource designed to help teams build more reliable AI systems through better evaluation practices.
What Are Eval Skills?
Eval skills are structured capabilities that guide coding agents through the process of setting up, auditing, and improving evaluation frameworks. They are distilled from patterns and mistakes observed across more than 50 companies and 4,000+ students — making them a practitioner-tested starting point rather than theoretical guidance.
The starting point is an eval-audit skill: a diagnostic tool that inspects your existing eval setup, runs checks across six key areas, and produces a prioritized list of problems to address. Think of it as a health check for your AI evaluation infrastructure.
Why This Matters Now
The role of coding agents has expanded significantly. They are no longer just writing code — they are instrumenting applications, running experiments, analyzing data, and building interfaces. As agents take on more of the measurement and iteration work, the quality of your evaluation setup becomes a direct constraint on how fast and safely you can move.
These skills complement vendor MCP (Model Context Protocol) servers by giving agents access to traces and experiments — the raw material needed to assess whether an AI system is actually working.
The Core Principle
Evals are the foundation of reliable AI deployment. Without a rigorous evaluation framework, teams are flying blind: shipping models and agents without a clear signal on whether performance is improving or degrading. Husain's resource operationalizes this principle by putting eval capability directly into the hands of the agents doing the work.
Key Takeaway for AI Teams
If your team is deploying AI agents in any capacity, the first question to answer is not 'which model?' but 'how will we know if it's working?' Evals Skills gives your engineering team a structured, peer-reviewed starting point to answer that question — and to keep answering it as your systems evolve.