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

Choosing an executor 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.
"""