Sportstime/.planning/phases/01-script-architecture/01-02-PLAN.md

phase, plan, type

phase	plan	type
01-script-architecture	02	execute

Extract NBA and NHL scrapers to dedicated sport modules.

Purpose: Continue the modular pattern established in Plan 01. Output: Scripts/nba.py and Scripts/nhl.py with respective scrapers.

<execution_context> @/.claude/get-shit-done/workflows/execute-phase.md @/.claude/get-shit-done/templates/summary.md </execution_context>

@.planning/PROJECT.md @.planning/ROADMAP.md @.planning/STATE.md

Prior work: @.planning/phases/01-script-architecture/01-01-SUMMARY.md

Source files: @Scripts/core.py @Scripts/scrape_schedules.py

Task 1: Create nba.py sport module Scripts/nba.py Create `Scripts/nba.py` following the mlb.py pattern:

1. Import from core:

   from core import Game, Stadium, ScraperSource, StadiumScraperSource, fetch_page, scrape_with_fallback, scrape_stadiums_with_fallback
   

2. NBA game scrapers:

   • scrape_nba_basketball_reference(season: int) -> list[Game]
   • scrape_nba_espn(season: int) -> list[Game]
   • scrape_nba_cbssports(season: int) -> list[Game]

3. NBA stadium scrapers:

   • scrape_nba_stadiums() -> list[Stadium] (from generate_stadiums_from_teams or hardcoded)

4. Source configurations:

   • NBA_GAME_SOURCES list of ScraperSource
   • NBA_STADIUM_SOURCES list of StadiumScraperSource

5. Convenience functions:

   • scrape_nba_games(season: int) -> list[Game]
   • get_nba_season_string(season: int) -> str - returns "2024-25" format

Copy exact parsing logic including team abbreviations and venue mappings from scrape_schedules.py. python3 -c "from Scripts.nba import scrape_nba_games, NBA_GAME_SOURCES; print('OK')" nba.py exists, imports from core.py, exports NBA scrapers

Task 2: Create nhl.py sport module Scripts/nhl.py Create `Scripts/nhl.py` following the same pattern:

1. Import from core:

   from core import Game, Stadium, ScraperSource, StadiumScraperSource, fetch_page, scrape_with_fallback, scrape_stadiums_with_fallback
   

2. NHL game scrapers:

   • scrape_nhl_hockey_reference(season: int) -> list[Game]
   • scrape_nhl_api(season: int) -> list[Game]
   • scrape_nhl_espn(season: int) -> list[Game]

3. NHL stadium scrapers:

   • scrape_nhl_stadiums() -> list[Stadium]

4. Source configurations:

   • NHL_GAME_SOURCES list of ScraperSource
   • NHL_STADIUM_SOURCES list of StadiumScraperSource

5. Convenience functions:

   • scrape_nhl_games(season: int) -> list[Game]
   • get_nhl_season_string(season: int) -> str - returns "2024-25" format

Copy exact parsing logic from scrape_schedules.py. python3 -c "from Scripts.nhl import scrape_nhl_games, NHL_GAME_SOURCES; print('OK')" nhl.py exists, imports from core.py, exports NHL scrapers

Before declaring plan complete: - [ ] `Scripts/nba.py` exists and imports cleanly - [ ] `Scripts/nhl.py` exists and imports cleanly - [ ] No syntax errors: `python3 -m py_compile Scripts/nba.py Scripts/nhl.py` - [ ] Both import from core.py (not duplicating shared utilities)

<success_criteria>

• nba.py contains all NBA-specific scrapers
• nhl.py contains all NHL-specific scrapers
• Both follow the pattern established in mlb.py
• All files import without errors </success_criteria>

After completion, create `.planning/phases/01-script-architecture/01-02-SUMMARY.md`