Overview
Large language models are flexible and probabilistic. If given a loosely scoped task, they may approach it differently on different runs. That flexibility is useful for creative reasoning but undesirable when a validated process should be repeated consistently.
An agent skill narrows the range of possible behavior by defining a repeatable workflow. It functions like a standard operating procedure that the agent can invoke when a matching task appears.
A skill commonly includes:
- A name.
- A description explaining when it should be used.
- Instructions for carrying out the workflow.
- Optional tool requirements.
- Optional metadata.
The top section may use a structured header separated by hyphens. This header identifies the skill and allows the agent to know that the capability exists without loading the entire file immediately.
Examples of skills used throughout the module include:
- PDF processing.
- Algorithmic art generation.
- Prompt contracts.
- Reverse prompting.
- Multi-agent orchestration.
- Stochastic multi-agent consensus.
- Model chat or agent debate.
- Agent review.
- Video-to-action conversion.
- Multi-agent browser automation.
A successful prompt can therefore be converted into infrastructure. Instead of copying a long set of instructions every time, the user saves the validated process as a skill. The agent can run the same process repeatedly and share it across projects.