Agile V Skills addresses a critical gap in AI-assisted software development: ensuring that AI-generated code is independently verified and traceable to requirements, rather than relying on the same AI agent to both write and test code (which introduces confirmation bias).
A former backend lead at Manus proposes replacing traditional function-calling in LLM agents with a single Unix-style run(command="...") tool that leverages pipes and shell operators, arguing that LLMs are naturally aligned with CLI patterns they've seen extensively in training data and that this approach reduces cognitive load on tool selection while enabling composition.
This article introduces golden sets—structured regression testing frameworks for probabilistic AI workflows that combine representative test cases, explicit scoring rubrics, and versioned evaluation contracts to detect regressions across prompt, model, retrieval, and policy changes before production impact.