Large language models (LLMs) are essentially powerful reasoning engines. While they are exceptional at language and logic, they are naturally limited by their training data. Skills—often referred to as tools, extensions, or plugins—act as the functional bridge between the AI’s “brain” and the external world.
How skills work
A skill allows an AI to perform actions beyond generating text. When you ask a question that requires real-time data or specialized computation, the model recognizes it cannot solve the prompt with internal knowledge alone. It then calls upon a specific skill to execute the task.
Common skill categories include:
- Real-time retrieval: Accessing the live internet to provide current news, weather, or stock prices.
- Computation and data analysis: Running code in a secure environment to solve complex math or visualize data.
- Application integration: Interacting with external software like Google Workspace, Slack, or GitHub to manage files and communications.
- Multimodal generation: Creating original images, video, or music based on text descriptions.
Terminology across platforms
Different AI providers use varying names for these capabilities, though they serve the same fundamental purpose:
| Provider | Terminology |
| Google Gemini | Extensions and Tools |
| Anthropic Claude | Skills and Analysis Tool |
| OpenAI ChatGPT | GPTs and Tools |
| Perplexity | Pro Search |
Why skills are essential
Skills transform an AI from a static encyclopedia into a dynamic assistant. Instead of simply explaining a concept, an LLM with the right skills can research a topic, calculate the results, and generate a final report or visual asset. This shift from information retrieval to task execution is what allows AI to function as a true productivity partner.
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