Calibrate

The outer loop of the dual loop (the design is docs/dual-loop.md in the System One Harness repository): a reasoning harness that improves another harness's configuration against the objective that harness's package declares, one change per version, validated by that harness's own model, with the evidence in a ledger.

Install this package on a reasoning harness (a coding base) with platform access for one inner harness: HR_API_URL, HR_CALIBRATION_TOKEN (a per-turn credential scoped to that harness) and HR_INNER_HARNESS (its id). The Skill carries the method; the scripts do the platform work:

Script	What it does
bench.py --runs 3 --package <dir>	K runs, one at a time; fetches each run's workspace; the objective's scoreboard and failure groups
fetch.py --out package	the inner harness's package (the one carrying the environment), never the harness's export
probe.py "a,b,c"	one run driven by a fixed action sequence, to measure the environment
publish.py --package <dir>	uploads the package as the inner harness's plugin; its instructions follow config.yaml
report.py --package <dir> traces...	the report over traces already on disk

The inner harness's package must carry config.yaml (with an objective) and ledger.jsonl; the harness must write trace.json into its session workspace, and its environment may archive what it showed under observations/. The Super Mario kit is the first such package.