Remigiusz Samborski (Lead DevRel Engineer, Google Cloud) takes you behind the scenes of Google Agent Skills — the open-source, structured instructions that encode Google Cloud domain knowledge for AI coding agents. The project started as a “swarm” effort before Google Cloud Next 2026, hit 15,000+ GitHub stars on launch, and then had to solve the real problem: scaling without losing quality. His answer is a four-part machine: a standardized repository layout for every skill (with a strong preference for remote MCP tools over CLI/API calls, for built-in auth and IAM governance); automated CI/CD checks on check-in (linters for frontmatter/naming/structure, link checkers to kill 404s and hallucinated links, AI-assisted guardrail checklists); continuous evals — on-submit suites authors must provide, plus weekly scheduled evals comparing agent performance with vs. without each skill across accuracy and efficiency (tokens + time), run against multiple agent frameworks for statistical significance; and a governance model where skills are products, not snippets — repo maintainers own the pipeline, skill owners own their skill’s long-term maintenance when APIs or models change. He also built agentic authoring tools (ADK multi-agent loops for self-critique) and a parallel internal “DevRel Skills” initiative for team workflows.
Behind the Scenes of Google Agent Skills — Build, Test, Scale
Remigiusz Samborski (Google Cloud DevRel) on how Google builds, tests, and scales Agent Skills: standardized SKILL.md layout, remote MCP tools, CI/CD checks, and continuous evals.