Agent-evals – Claude skill to build your own evals
I’ve spent the past 10 years working on AI in finance, with much of that time focused on building evaluation systems for production environments. As agents become more widely adopted, more software engineering and product people have start building them. But I’ve noticed that many teams are not yet fluent in systematic evaluation, or in the processes needed to keep agent quality high over time. For large organizations, that gap is rarely the bottleneck due to dedicated teams. But after speaking with a number of startups, it became clear that building strong, up-to-date evals is much harder…
Agent-evals – Claude skill to build your own evals launched on May 4, 2026 with 9 votes, #1,305 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Your own eval engineer. Contribute to fsilavong/agent-eval development by creating an account on GitHub.
Agent Eval is a skill for evaluating agentic AI pipeline systems at both the component level and end-to-end level. It helps you define what to measure, build or sample eval cases, run repeatable tests, track regressions over time, and turn results into grounded takeaways about what improved, what regressed, and what to change next. git clone https://github.com/fsilavong/agent-eval.git ~ /.claude/skills/agent-eval Install with the Vercel Skills CLIfrom github.com
In the maker’s words, at launch
I’ve spent the past 10 years working on AI in finance, with much of that time focused on building evaluation systems for production environments. As agents become more widely adopted, more software engineering and product people have start building them. But I’ve noticed that many teams are not yet fluent in…
Does a similar job
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