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toolformer-pattern

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Teach language models to decide when to call tools — API invocation learned from self-supervised execution feedback.

skills/toolformer-pattern/SKILL.md

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## Overview

The Toolformer pattern trains a language model to use external tools — calculators,
search engines, calendars, code interpreters — by deciding for itself when a tool
call would help. The original method: annotate text with candidate API calls,
execute them, and keep only the calls whose results actually improve the model's
predictions. The model learns a policy over tool use from this filtered data: not
just how to format calls, but when they're worth making.

As a design pattern (beyond the original paper), "Toolformer-style" means any
system where tool use is learned or prompted as a decision, not hardcoded. The
model sees tool descriptions, generates calls interleaved with text using special
tokens or structured formats, receives execution results, and continues. Modern
agent frameworks implement this with function calling, but the underlying