When I first saw Anthropic's 'Skills' feature, what came to mind wasn't technical parameters, but how it changes the interaction between AI and humans.

Traditional AI model updates require retraining the entire network, which is time-consuming and resource-intensive. The Skills feature allows AI to improve its capabilities just like humans, through the accumulation of experience. Robert Nishihara is right—this is indeed a significant step toward continuous learning.

I built an advanced skill in Claude Code to monitor my interactions with it, record key feedback, and then transform this experience into new skills or optimize existing ones as needed.

For example, when I notice Claude making an error on a programming task, I don't have to explain it repeatedly each time. The skill system records the error and the correction method, so the next time a similar situation arises, Claude can automatically apply the correct solution.


The benefits of this learning approach are very practical:

**Knowledge is Explainable**: Skills are stored in plain text, allowing you to directly view and understand what the AI has learned.

**Easy to Correct**: When errors are found, you can simply modify the skill file without needing to retrain the model.

**Data Efficient**: Each skill is a refined essence of knowledge, more valuable than raw data.

Some question whether this is just a variation of RAG. The difference is that skills are not simple document retrieval but knowledge modules processed through reasoning. As Jason said, this is more like AI's 'muscle memory'—the more you use it, the more proficient it becomes.


In practical use, the Skills feature is changing my workflow. I can build a transcription system媲mable to professional tools in just an afternoon, something that previously took weeks. More importantly, this tool continuously optimizes itself with use.

Of course, the skill system still has a long way to go. Version control, testing frameworks, and retrieval mechanisms all need further refinement. But the core idea is right: to make AI's learning process closer to humans, more natural, and more efficient.

This change is not just an improvement in technical parameters but a fundamental shift in interaction methods. AI is no longer just a tool that passively responds to commands but a partner that can accumulate experience and self-optimize.