docs(02): create stadium foundation phase plans

Phase 2: Stadium Foundation
- 2 plans created
- 5 total tasks defined
- Ready for execution

Plan 02-01: Audit & complete hardcoded stadium data
Plan 02-02: Regenerate canonical data and verify pipeline

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.planning/phases/02-stadium-foundation/02-01-PLAN.md

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+---
+phase: 02-stadium-foundation
+plan: 01
+type: execute
+---
+
+<objective>
+Audit and complete hardcoded stadium data across all 4 sport modules.
+
+Purpose: Ensure all sport modules have complete, accurate stadium data that will flow through the canonicalization pipeline.
+Output: All 4 sport modules with complete stadium data (city, state, lat/lng, capacity, year_opened, teams).
+</objective>
+
+<execution_context>
+~/.claude/get-shit-done/workflows/execute-phase.md
+~/.claude/get-shit-done/templates/summary.md
+</execution_context>
+
+<context>
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+@.planning/phases/01-script-architecture/01-03-SUMMARY.md
+
+**Key files:**
+@Scripts/mlb.py
+@Scripts/nba.py
+@Scripts/nhl.py
+@Scripts/nfl.py
+
+**Current state:**
+- MLB, NBA, NHL, NFL modules have hardcoded stadium data with city, state, lat/lng, capacity, teams
+- Missing field: year_opened (null in all canonical data)
+- NFL module created in Phase 1 Plan 03 with 30 hardcoded stadiums
+- Bundled stadiums_canonical.json has incomplete data (state="", capacity=0, missing NFL)
+
+**Expected stadium counts:**
+- MLB: 30 stadiums (30 teams)
+- NBA: 30 stadiums (30 teams)
+- NHL: 32 stadiums (32 teams)
+- NFL: 30 stadiums (32 teams, 2 shared: SoFi Stadium, MetLife Stadium)
+
+**Stadium data structure:**
+Each module has `scrape_{sport}_stadiums_hardcoded()` returning Stadium objects with:
+- name, city, state, lat/lng, capacity, teams
+- Missing: year_opened for filtering historical/renamed venues
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Audit stadium data completeness across all 4 sport modules</name>
+  <files>Scripts/mlb.py, Scripts/nba.py, Scripts/nhl.py, Scripts/nfl.py</files>
+  <action>
+1. Read each sport module's hardcoded stadium function
+2. Create audit report listing for each sport:
+   - Stadium count (should match expected)
+   - Fields present/missing
+   - Any stadiums with missing lat/lng (should be 0)
+   - Any stadiums with missing capacity (should be 0)
+3. Identify gaps: stadiums missing from lists, incorrect coordinates, missing teams
+
+Do NOT modify any files in this task - audit only. The goal is to understand current state before making changes.
+  </action>
+  <verify>Print audit summary showing stadium counts per sport and any data quality issues found</verify>
+  <done>Audit report shows MLB:30, NBA:30, NHL:32, NFL:30 stadiums with all required fields documented</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Add year_opened to all hardcoded stadiums</name>
+  <files>Scripts/mlb.py, Scripts/nba.py, Scripts/nhl.py, Scripts/nfl.py</files>
+  <action>
+Add year_opened to each stadium's hardcoded data. Use the actual opening year for each venue:
+
+**MLB stadiums (sample):**
+- Fenway Park: 1912
+- Wrigley Field: 1914
+- Dodger Stadium: 1962
+- Globe Life Field: 2020
+
+**NBA arenas (sample):**
+- TD Garden: 1995
+- Madison Square Garden: 1968
+- Chase Center: 2019
+- Intuit Dome: 2024
+
+**NHL arenas:** Many share with NBA - verify and match
+
+**NFL stadiums (sample):**
+- Lambeau Field: 1957
+- SoFi Stadium: 2020
+- Allegiant Stadium: 2020
+
+For each module:
+1. Update the hardcoded dict to include 'year_opened' key
+2. Update Stadium object creation to include year_opened parameter
+3. Ensure Stadium dataclass in core.py has year_opened field (verify first)
+
+Research actual opening years from Wikipedia if unsure. Use the original opening year, not renovation years.
+  </action>
+  <verify>Run `python -c "from mlb import scrape_mlb_stadiums; s=scrape_mlb_stadiums(); print(f'MLB: {len(s)} stadiums, year_opened example: {s[0].year_opened if hasattr(s[0], \"year_opened\") else \"MISSING\"}')"` for each sport</verify>
+  <done>All 4 sport modules have year_opened in hardcoded data, Stadium objects include year_opened field</done>
+</task>
+
+</tasks>
+
+<verification>
+Before declaring plan complete:
+- [ ] Audit confirms expected stadium counts: MLB:30, NBA:30, NHL:32, NFL:30
+- [ ] All 4 modules have year_opened in hardcoded stadium data
+- [ ] No Python syntax errors in any module
+- [ ] Stadium dataclass supports year_opened field
+</verification>
+
+<success_criteria>
+
+- Task 1: Audit complete with documented counts and any gaps identified
+- Task 2: year_opened added to all hardcoded stadiums in all 4 modules
+- No import errors when loading modules
+- Ready for Plan 02 (pipeline regeneration)
+</success_criteria>
+
+<output>
+After completion, create `.planning/phases/02-stadium-foundation/02-01-SUMMARY.md`:
+
+# Phase 2 Plan 01: Stadium Data Audit & Completion Summary
+
+**[Substantive one-liner]**
+
+## Accomplishments
+
+- [Stadium counts verified]
+- [year_opened added to all modules]
+
+## Files Created/Modified
+
+- `Scripts/mlb.py` - Added year_opened
+- `Scripts/nba.py` - Added year_opened
+- `Scripts/nhl.py` - Added year_opened
+- `Scripts/nfl.py` - Added year_opened
+
+## Decisions Made
+
+[Any gaps found and how resolved]
+
+## Issues Encountered
+
+[Any data issues discovered]
+
+## Next Step
+
+Ready for 02-02-PLAN.md (pipeline regeneration)
+</output>