How long can your skills be before your agent forgets what you told it? — Laurie Voss, Arize AI
Instruction capacity rose sharply, but long skill files still need evals. Current models can track thousands of constraints, with large differences by model, wording, and order.
A rerun of IFScale reproduced the old failure point around **200–300 rules**. Current frontier models reached roughly **2,000–5,000 rules** before major decline; GPT-5.5 reportedly held **99% accuracy at 5,000 rules** on this proxy task.
Revisit prompt-length assumptions before splitting a style guide or policy into many agents. Capacity is less likely to be the first constraint now, so test whether one coherent skill is cheaper and easier to maintain than a handoff-heavy design.
A rerun of IFScale reproduced the old failure point around **200–300 rules**. Current frontier models reached roughly **2,000–5,000 rules** before major decline; GPT-5.5 reportedly held **99% accuracy at 5,000 rules** on this proxy task. Revisit prompt-length assumptions before splitting a style guide or policy into many agents. Capacity is less likely to be the first constraint now, so test whether one coherent skill is cheaper and easier to maintain than a handoff-heavy design. IFScale checks whether required words appear in a synthetic report, not whether an agent reasons well. Reported limits ranged from **750 to 9,000-plus rules**, and performance can shift with instruction wording or order, making output verification essential.
This raises the estimated instruction-capacity ceiling and weakens capacity alone as a reason to split coherent guidance across agents. It does not show that long skills improve real agent work: IFScale measures synthetic rule adherence, and ordering and wording still matter. Against the prior candidates, the practical conclusion is to compare one maintained skill with handoff-heavy designs using repeated, architecture-relevant regression tests.