feat(orchestrator): ADP-derived SFT cold-start data pipeline

Train a single ~9B orchestrator (Qwen3-8B) for local<->cloud routing via SFT
on synthetic traces derived from NeuLab ADP (agent-data-collection).

- sft_data/: full ADP trajectory loader, tier model + pricing, 4 paradigm
  renderers, reward-ranked selection, conversations-JSONL serializer, CLI
- wire sft_trainer._generate_traces() to the pipeline (no GPU / no API keys)
- Qwen3-8B SFT config + scripts/orchestrator CLI wrapper
- offline fixture tests (12)

Heuristic competence labels for the cold-start; real paradigm execution +
GRPO are the documented v2.

scripts/orchestrator/build_sft_data.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+"""Thin CLI wrapper: build the orchestrator SFT dataset from NeuLab ADP.
+
+Equivalent to::
+
+    python -m openjarvis.learning.intelligence.orchestrator.sft_data.build [...]
+
+Usage:
+    python scripts/orchestrator/build_sft_data.py \
+        --out data/orchestrator_sft_traces.jsonl \
+        --max-tasks 2000 --adp-configs codeactinstruct,code_feedback,openhands
+"""
+
+from __future__ import annotations
+
+from openjarvis.learning.intelligence.orchestrator.sft_data.build import _main
+
+if __name__ == "__main__":
+    raise SystemExit(_main())