Unify harness L-C-A on the canonical lca.WorkloadProfile

Phase 0 of the two-stop work. The prompt block labeled `workload_lca_profile`
previously re-derived L-C-A from summarize_window's ad-hoc percentiles, diverging
from the paper's 10-dim RobustScaler vector implemented in lca.py. Make that block
authoritative: build_harness_context now accepts an optional workload_profile and
renders the canonical 10-dim vector + per-family stats when present, falling back
to the legacy rendering only when no profile is supplied (direct unit-test calls).

Real call sites (study prompt/llm-propose/tune, run_baseline_then_llm) build the
profile via lca.build_study_workload_profile and pass it through build_prompt. The
heuristic regime classifiers keep reading window_summary; that is the heuristic
layer, distinct from the similarity metric.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/aituner/cli.py

@@ -14,6 +14,7 @@ from .harness import (
 )
 from .job import append_job, build_trial_job
 from .lca import (
+    build_study_workload_profile,
     build_workload_profile,
     resolve_length_mode,
     similarity_report,
@@ -140,6 +141,7 @@ def cmd_study_prompt(args: argparse.Namespace) -> int:
         window_summary=summarize_window(requests, window),
         state=state,
         capability_profile=capability_profile,
+        workload_profile=build_study_workload_profile(study, requests, window),
     )
     prompt_name = args.prompt_name or f"prompt-{state.next_trial_index:04d}"
     path = store.write_prompt(study.study_id, prompt_name, prompt)
@@ -160,6 +162,7 @@ def cmd_study_llm_propose(args: argparse.Namespace) -> int:
         window_summary=summarize_window(requests, window),
         state=state,
         capability_profile=capability_profile,
+        workload_profile=build_study_workload_profile(study, requests, window),
     )
     proposal_text = call_llm_for_proposal(
         policy=study.llm,
@@ -242,11 +245,13 @@ def cmd_study_tune(args: argparse.Namespace) -> int:
             break
         window, requests = load_trace_requests(study, study_spec_path=spec_path)
         window_summary = summarize_window(requests, window)
+        workload_profile = build_study_workload_profile(study, requests, window)
         harness_context = (
             build_harness_context(
                 study=study,
                 window_summary=window_summary,
                 state=state,
+                workload_profile=workload_profile,
             )
             if study.llm.use_harness
             else None
@@ -256,6 +261,7 @@ def cmd_study_tune(args: argparse.Namespace) -> int:
             window_summary=window_summary,
             state=state,
             capability_profile=capability_profile,
+            workload_profile=workload_profile,
         )
         prompt_name = f"prompt-{state.next_trial_index:04d}"
         store.write_prompt(study.study_id, prompt_name, prompt)