60b450d869
- 01-01-PLAN.md: core.py + mlb.py (executed) - 01-02-PLAN.md: nba.py + nhl.py - 01-03-PLAN.md: nfl.py + orchestrator refactor - Codebase documentation for planning context Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
120 lines
3.6 KiB
Markdown
120 lines
3.6 KiB
Markdown
---
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phase: 01-script-architecture
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plan: 02
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type: execute
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---
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<objective>
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Extract NBA and NHL scrapers to dedicated sport modules.
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Purpose: Continue the modular pattern established in Plan 01.
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Output: `Scripts/nba.py` and `Scripts/nhl.py` with respective scrapers.
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</objective>
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<execution_context>
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@~/.claude/get-shit-done/workflows/execute-phase.md
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@~/.claude/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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**Prior work:**
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@.planning/phases/01-script-architecture/01-01-SUMMARY.md
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**Source files:**
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@Scripts/core.py
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@Scripts/scrape_schedules.py
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</context>
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<tasks>
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<task type="auto">
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<name>Task 1: Create nba.py sport module</name>
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<files>Scripts/nba.py</files>
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<action>
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Create `Scripts/nba.py` following the mlb.py pattern:
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1. Import from core:
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```python
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from core import Game, Stadium, ScraperSource, StadiumScraperSource, fetch_page, scrape_with_fallback, scrape_stadiums_with_fallback
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```
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2. NBA game scrapers:
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- `scrape_nba_basketball_reference(season: int) -> list[Game]`
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- `scrape_nba_espn(season: int) -> list[Game]`
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- `scrape_nba_cbssports(season: int) -> list[Game]`
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3. NBA stadium scrapers:
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- `scrape_nba_stadiums() -> list[Stadium]` (from generate_stadiums_from_teams or hardcoded)
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4. Source configurations:
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- `NBA_GAME_SOURCES` list of ScraperSource
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- `NBA_STADIUM_SOURCES` list of StadiumScraperSource
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5. Convenience functions:
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- `scrape_nba_games(season: int) -> list[Game]`
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- `get_nba_season_string(season: int) -> str` - returns "2024-25" format
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Copy exact parsing logic including team abbreviations and venue mappings from scrape_schedules.py.
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</action>
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<verify>python3 -c "from Scripts.nba import scrape_nba_games, NBA_GAME_SOURCES; print('OK')"</verify>
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<done>nba.py exists, imports from core.py, exports NBA scrapers</done>
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</task>
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<task type="auto">
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<name>Task 2: Create nhl.py sport module</name>
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<files>Scripts/nhl.py</files>
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<action>
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Create `Scripts/nhl.py` following the same pattern:
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1. Import from core:
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```python
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from core import Game, Stadium, ScraperSource, StadiumScraperSource, fetch_page, scrape_with_fallback, scrape_stadiums_with_fallback
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```
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2. NHL game scrapers:
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- `scrape_nhl_hockey_reference(season: int) -> list[Game]`
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- `scrape_nhl_api(season: int) -> list[Game]`
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- `scrape_nhl_espn(season: int) -> list[Game]`
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3. NHL stadium scrapers:
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- `scrape_nhl_stadiums() -> list[Stadium]`
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4. Source configurations:
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- `NHL_GAME_SOURCES` list of ScraperSource
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- `NHL_STADIUM_SOURCES` list of StadiumScraperSource
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5. Convenience functions:
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- `scrape_nhl_games(season: int) -> list[Game]`
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- `get_nhl_season_string(season: int) -> str` - returns "2024-25" format
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Copy exact parsing logic from scrape_schedules.py.
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</action>
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<verify>python3 -c "from Scripts.nhl import scrape_nhl_games, NHL_GAME_SOURCES; print('OK')"</verify>
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<done>nhl.py exists, imports from core.py, exports NHL scrapers</done>
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</task>
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</tasks>
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<verification>
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Before declaring plan complete:
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- [ ] `Scripts/nba.py` exists and imports cleanly
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- [ ] `Scripts/nhl.py` exists and imports cleanly
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- [ ] No syntax errors: `python3 -m py_compile Scripts/nba.py Scripts/nhl.py`
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- [ ] Both import from core.py (not duplicating shared utilities)
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</verification>
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<success_criteria>
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- nba.py contains all NBA-specific scrapers
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- nhl.py contains all NHL-specific scrapers
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- Both follow the pattern established in mlb.py
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- All files import without errors
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</success_criteria>
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<output>
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After completion, create `.planning/phases/01-script-architecture/01-02-SUMMARY.md`
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</output>
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