I think that what they mean is that instead of ten perfectly orthogonal "unix philosophy" tools (skills) for the agent to compose when solving a problem, each with an API surface (description text) the size of Texas, you'd want to can each composition in a shell script (or a bespoke rust binary, if you enjoy watching your bot perform some heavy lifting) that only solves one problem but solves it so focused that the accompanying skill description barely consumes more context than the tool's self descriptive name.
I still didn't follow, you mean to pipe things between tool calls? Like if you want to query something and then update another without the intermediate getting brought in context?
Instead of requiring each session to understand the n tools used to solve a particular problem, you bundle up the solution in a conventional script (that's what I meant by "can", as in canning) that the agent can use with very little documentation in the context. When the model is smart enough to figure out the composition of underlying tools during regular execution, it will also be able to do the canning up as a script and write the lightweight documentation that turns the script into a skill. Subsequent use will only require that lightweight documentation in context.
Skill still needs to be loaded in context, what would it change?
I think that what they mean is that instead of ten perfectly orthogonal "unix philosophy" tools (skills) for the agent to compose when solving a problem, each with an API surface (description text) the size of Texas, you'd want to can each composition in a shell script (or a bespoke rust binary, if you enjoy watching your bot perform some heavy lifting) that only solves one problem but solves it so focused that the accompanying skill description barely consumes more context than the tool's self descriptive name.
I still didn't follow, you mean to pipe things between tool calls? Like if you want to query something and then update another without the intermediate getting brought in context?
Instead of requiring each session to understand the n tools used to solve a particular problem, you bundle up the solution in a conventional script (that's what I meant by "can", as in canning) that the agent can use with very little documentation in the context. When the model is smart enough to figure out the composition of underlying tools during regular execution, it will also be able to do the canning up as a script and write the lightweight documentation that turns the script into a skill. Subsequent use will only require that lightweight documentation in context.