"""The driver benchmark: canned milestones, run repeatedly per model.

Choosing a driver model, and later tuning its knobs, should rest on
measured success rates, not on one run or on reputation. Each task is a fixed
approved plan over a seed project, with a hidden check the model never sees;
success means the check passes, not that the model said it finished. Runs use
the agent's own client wiring (context budget, declared limits, tool_choice
only where declared), so what is measured is what the agent would do.

Results are stored, one JSON line per run, with everything needed to compare
them later: the model's identity and declared facts, a note on how it was
served, the task's content hash, the agent's commit, and its tuning. A new
model then needs only its own runs to be compared with everything measured
before it.

See scripts/benchmark.py, and docs/Tuning-Guide.md for how to read results.
"""