Closing the knowledge gap with agent skills

Large language models (LLMs) have fixed knowledge, being trained at a specific point in time. Software engineering practices are fast paced and change often, where new libraries are launched every day and best practices evolve quickly. This leaves a knowledge gap that language models can't solve on their own. At Google DeepMind we see this in a few ways: our models don't know about themselves when they're trained, and they aren't necessarily aware of subtle changes in best practices (like thought circulation) or SDK changes. Many solutions exist, from web search tools to dedicated MCP services, but more recently, agent skills have surfaced as an extremely lightweight but potentially effective way to close this gap. While there are strategies that we, as model builders, can implement, we wanted to explore what is possible for any SDK maintainer. Read on for what we did to build the Gemini API developer skill and the results it had on performance. What we built To help coding agents buil