Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research

Empirical economists inherit a toolbox. Shared packages, replication archives, and circulated guides etc. Theorists largely start from a blank page. By 2026, large language models can produce and check nontrivial mathematics, so the binding constraint on machine-assisted theory is no longer production but trust: a fluent model will prove a false theorem as readily as a true one. I propose a verification-first protocol for doing economic theory with a language model and instantiate it as three reusable methods that differ on a single axis, how the work is checked: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting, the author triaging), and a structured multi-agent project with a reviewer gate. I evaluate the protocol on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans-Kominers eigengrade model of grade inflation; none of the three runs produced a strict direct-revelation VCG/Clarke mechanism, a point the adversarial pass itself established. The evidence is a single worked example with one model pairing run by one operator, so what follows are demonstrations rather than measured effects. Three phenomena recur. First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins. Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal. Third, polish is not rigor: the most finished-looking output was the least verified. The methodological takeaway is that external verification, not model capability, is the design variable.