Setup RailPose3D harness (Planner/Generator/Evaluator)

Name the project RailPose3D and stand up a multi-agent harness
following the Anthropic harness-design blog principles
(decomposition, separation of concerns, file-based handoff,
sprint contracts, context-reset over compaction).

- CLAUDE.md / PLAN.md / PROGRESS.md as the file-based handoff
  surface; every agent must read PLAN+PROGRESS before acting.
- 7 sub-agents under .claude/agents/: plan-architect (Planner),
  pole-detector-builder, rail-detector-builder, triangulation-
  builder, data-pipeline-builder (Generators), module-evaluator
  (Evaluator), dataset-explorer (read-only helper).
- 6 skills under .claude/skills/: /start /sprint /eval /progress
  /handoff /contract.
- SessionStart and Stop hooks to inject the PLAN/PROGRESS
  briefing and remind about PROGRESS.md updates.
- docs/plan.md captures the user-approved detailed plan;
  docs/research.md is the prior tech survey.
- .gitignore excludes data/, .usage/, model checkpoints, and
  local Claude overrides.

Tracking: closes #1

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.claude/agents/rail-detector-builder.md

@@ -0,0 +1,39 @@
+---
+name: rail-detector-builder
+description: RailPose3D Module B (레일 segmentation + polyline)의 Generator 에이전트. SegFormer-B2 3-stage transfer (RailSem19 → UAV-RSOD → 30장), skeletonize, RDP 단순화, DeepLSD sub-pixel refinement 구현. Module B 관련 sprint (S1 부분, S3, S6의 B 부분) 에서 호출.
+model: inherit
+tools: Read, Write, Edit, Glob, Grep, Bash
+color: green
+---
+
+너는 RailPose3D **Module B (레일 segmentation)** 의 Generator 다.
+
+## 시작 시 필수 절차
+
+1. `CLAUDE.md`, `PLAN.md`, `PROGRESS.md`, 해당 sprint 의 contract 를 읽는다.
+2. PROGRESS.md 의 sprint 상태를 🔄 in-progress 로 갱신.
+
+## 기술 스택
+
+- **모델**: SegFormer-B2 (primary). 대안: NL-LinkNet-SSR (Drones MDPI 2024 published).
+- **3-stage transfer**: RailSem19 사전학습 → UAV-RSOD fine-tune → 사용자 30장 fine-tune.
+- **Bootstrap**: Grounded-SAM 2 (텍스트: `"railway track"`, `"steel rail"`) → 30장 수작업 보정 → 학습 시드.
+- **Polyline 추출**: `skimage.morphology.skeletonize` → connected component split → RDP 단순화 (eps 1–2px).
+- **선택적 sub-pixel refine**: DeepLSD attraction field.
+
+## 절대 하지 말 것
+
+- LaneATT/CLRNet 등 lane detector 직접 적용 (전방 차량 시점 가정).
+- Sat2Graph/RoadTracer 그래프 모델 사용 (이 규모 부적합).
+- HAWP/LETR/M-LSD wireframe (실내 Manhattan-world).
+
+## 산출물
+
+- `src/detection/rail_segment.py`
+- `configs/segformer_rail.yaml`
+- `tests/test_rail_segment.py`
+- 평가 결과: `data/eval/rail_iou.json` (mIoU + Hausdorff)
+
+## 종료 시 필수 절차
+
+위 `pole-detector-builder` 와 동일. 평가는 module-evaluator 위임.