NBA Tracker

提供NBA球队和球员赛程、实时比分及关键时刻提醒，支持追踪球员伤病和自动添加比赛到日历，适合观赛辅助。

Security Scan

The skill's requirements and behavior align with its stated purpose (fetching NBA data via the nba_api library); it requests no credentials and has no unusual install steps, though the bundled examples and script contain sloppy bugs and a truncated calendar example that you should review before running.

Purpose & Capability

Name/description (NBA schedules, scores, injuries, calendar add) match the code and SKILL.md examples which use nba_api and pandas. Declared dependencies (nba_api, pandas) are appropriate and no unrelated credentials, binaries, or config paths are requested.

Instruction Scope

SKILL.md and code only call the nba_api endpoints and show examples for schedules, player stats, live scores and 'crunch time' alerts. One example (truncated) mentions adding games to Apple Calendar and earlier SKILL.md snippets import subprocess for that task — that could invoke system commands if followed, so review any calendar-related example code before running. Otherwise, examples do not ask the agent to read unrelated system files or exfiltrate data.

Install Mechanism

No install spec is provided; SKILL.md instructs users to pip install nba_api and pandas which is expected. No downloads from untrusted URLs or archive extraction steps are present.

Credentials

The skill requests no environment variables or credentials. The API usage relies on public nba_api library calls; optional proxy/custom headers are supported by the underlying library (user-supplied), which is reasonable for networking/troubleshooting.

Persistence & Privilege

always is false and the skill does not request persistent system-wide privileges or alter other skills. Model invocation is allowed (platform default) but there are no additional persistent actions in the files.

Assessment

This skill appears to be what it claims: an nba_api-based viewer for schedules, scores, injuries and simple alerts. Before installing or running it: 1) review/trim the truncated calendar example — any use of subprocess or system calls to modify calendars can run shell commands on your machine, so only run that part if you trust the code and understand the commands; 2) test the included script in a sandbox or non-production environment (there are some coding inconsistencies/typos that could raise exceptions); 3) since the skill uses live NBA endpoints, expect network requests and rate limits; 4) no credentials are required, which reduces risk, but always inspect third-party example code (especially anything that spawns subprocesses or writes to system calendars) before use.

NBA Game Viewing Assistant

Wrapper for nba_api to support game viewing scenarios:

• Track favorite players/teams
• View schedules and add to calendar
• Real-time scores
• Crunch time alerts (last 5 minutes + score difference < 5)

⚠️ Note: This tool is designed for viewing purposes. For prediction markets/betting use cases, consider more real-time professional data sources (e.g., Sportradar, Genius Sports) with lower latency and higher reliability requirements.

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Installation

pip install nba_api pandas

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Quick Start

Check Team Standings

from nba_api.stats.endpoints import leaguestandings

standings = leaguestandings.LeagueStandings()
df = standings.get_data_frames()[0]

# Check Hornets record
hornets = df[df['TeamName'] == 'Hornets']
print(hornets[['TeamName', 'WINS', 'LOSSES', 'WinPCT']])

View Today's Schedule

from nba_api.stats.endpoints import scoreboardv3
from datetime import datetime

today = datetime.now().strftime('%Y-%m-%d')
board = scoreboardv3.ScoreboardV3(game_date=today, league_id='00')
data = board.get_dict()

for game in data['scoreboard']['games']:
    home = game['homeTeam']
    away = game['awayTeam']
    print(f"{away['teamName']} @ {home['teamName']}")

Get Player Stats

from nba_api.stats.endpoints import playercareerstats
from nba_api.stats.static import players

# Find player ID
ball_info = players.find_players_by_full_name("LaMelo Ball")
player_id = ball_info[0]['id']

# Get career stats
career = playercareerstats.PlayerCareerStats(player_id=player_id)
df = career.season_totals_regular_season.get_data_frame()
print(df.tail())

Real-time Scores

from nba_api.live.nba.endpoints import scoreboard

games = scoreboard.ScoreBoard()
data = games.get_dict()

for game in data['scoreboard']['games']:
    home = game['homeTeam']['teamName']
    away = game['awayTeam']['teamName']
    home_score = game['homeTeam']['score']
    away_score = game['awayTeam']['score']
    status = game['gameStatusText']
    print(f"{away} @ {home}: {away_score}-{home_score} ({status})")

---

Example Use Cases

Example 1: Track a Specific Player

Scenario: Track LaMelo Ball's games and add to calendar

from nba_api.stats.static import players
from nba_api.stats.endpoints import playerinjuries, teamgamelog
from datetime import datetime, timedelta

# 1. Find player
ball = players.find_players_by_full_name("LaMelo Ball")[0]
print(f"Player: {ball['full_name']}")

# 2. Check injury status
try:
    injuries = playerinjuries.PlayerInjuries(player_id=ball['id'])
    injury_df = injuries.get_data_frames()[0]
    if not injury_df.empty:
        print(f"⚠️ Injury: {injury_df.iloc[0]['INJURY']}")
    else:
        print("✅ No injuries")
except:
    print("⚠️ Injury info not available")

# 3. Get upcoming games
gamelog = teamgamelog.TeamGameLog(
    team_id=ball['team_id'], 
    season='2025-26'
)
df = gamelog.get_data_frames()[0]

# Filter future games
today = datetime.now()
upcoming = []
for idx, row in df.iterrows():
    try:
        game_date = datetime.strptime(row['GAME_DATE'], '%Y-%m-%d')
        if game_date > today:
            upcoming.append({
                'date': row['GAME_DATE'],
                'matchup': row['MATCHUP']
            })
    except:
        continue

print(f"\nUpcoming games:")
for game in upcoming[:5]:
    print(f"  {game['date']}: {game['matchup']}")

Example 2: Crunch Time Alerts

Scenario: Get notified when game is in crunch time (last 5 min + close score)

from nba_api.live.nba.endpoints import scoreboard, playbyplay
import time
from datetime import datetime

def check_crunch_time():
    """Check for crunch time games (period >= 4, clock < 5:00, score diff <= 5)"""
    
    games = scoreboard.ScoreBoard()
    data = games.get_dict()
    
    crunch_games = []
    
    for game in data['scoreboard']['games']:
        if game['gameStatus'] != 2:  # Only live games
            continue
        
        home_score = game['homeTeam']['score']
        away_score = game['awayTeam']['score']
        score_diff = abs(home_score - away_score)
        
        if score_diff > 5:
            continue
        
        # Get detailed time from play-by-play
        try:
            pbp = playbyplay.PlayByPlay(game_id=game['gameId'])
            pbp_data = pbp.get_dict()
            
            if pbp_data['game']['actions']:
                last_action = pbp_data['game']['actions'][-1]
                period = last_action.get('period', 0)
                clock = last_action.get('clock', '12:00')
                
                # Only 4th quarter or OT
                if period >= 4:
                    mins, secs = clock.split(':')
                    remaining = int(mins) + int(secs) / 60
                    
                    if remaining < 5:
                        crunch_games.append({
                            'game': f"{game['awayTeam']['teamName']} @ {game['homeTeam']['teamName']}",
                            'score': f"{away_score} - {home_score}",
                            'clock': clock,
                            'period': period
                        })
        except:
            continue
    
    return crunch_games

# Monitor loop
print("🏀 Monitoring for crunch time games...")
notified = set()

while True:
    games = check_crunch_time()
    
    for game in games:
        key = f"{game['game']}_{game['clock']}"
        if key not in notified:
            print(f"\n🔥 CRUNCH TIME!")
            print(f"   {game['game']}")
            print(f"   Score: {game['score']}")
            print(f"   Time: {game['clock']} (Q{game['period']})")
            notified.add(key)
    
    time.sleep(30)  # Check every 30 seconds

Example 3: Add Games to Calendar

Scenario: Automatically add player's games to Apple Calendar

import subprocess
from datetime import datetime, timedelta

def add_game_to_calendar(player_name, game_date, matchup, calendar="NBA"):
    """Add game to Apple Calendar"""
    
    # Parse game time (assumes 7:00 PM local)
    game_time = datetime.strptime(game_date, '%Y-%m-%d')
    game_time = game_time.replace(hour=19, minute=0)
    
    title = f"🏀 {player_name}: {matchup}"
    end_time = game_time + timedelta(hours=2, minutes=30)
    
    script = f'''
    tell application "Calendar"
        tell calendar "{calendar}"
            make new event at end with properties {{summary:"{title}", start date:date "{game_time.strftime('%Y-%m-%d %H:%M:%S')}", end date:date "{end_time.strftime('%Y-%m-%d %H:%M:%S')}"}}
        end tell
    end tell
    '''
    
    try:
        subprocess.run(['osascript', '-e', script], check=True)
        print(f"✅ Added: {title} on {game_date}")
        return True
    except Exception as e:
        print(f"❌ Failed: {e}")
        return False

# Usage
add_game_to_calendar("LaMelo Ball", "2026-03-18", "Heat @ Hornets")

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Common Commands

# Hornets standings
python -c "from nba_api.stats.endpoints import leaguestandings; df = leaguestandings.LeagueStandings().get_data_frames()[0]; print(df[df['TeamName'] == 'Hornets'][['TeamName', 'WINS', 'LOSSES', 'WinPCT']])"

# Today's games
python -c "from nba_api.stats.endpoints import scoreboardv3; from datetime import datetime; board = scoreboardv3.ScoreboardV3(game_date=datetime.now().strftime('%Y-%m-%d'), league_id='00'); [print(f\"{g['awayTeam']['teamName']} @ {g['homeTeam']['teamName']}\") for g in board.get_dict()['scoreboard']['games']]"

# Player career stats
python -c "from nba_api.stats.static import players; from nba_api.stats.endpoints import playercareerstats; p = players.find_players_by_full_name('LaMelo Ball')[0]; df = playercareerstats.PlayerCareerStats(player_id=p['id']).season_totals_regular_season.get_data_frame(); print(df.tail())"

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Error Handling

from nba_api.stats.library.http import NBAHTTPError

try:
    # API call
    pass
except NBAHTTPError as e:
    print(f"NBA API Error: {e}")
except Exception as e:
    print(f"Unknown error: {e}")

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Important Notes

1. Rate Limiting: Free but may be throttled if too frequent. Cache data when possible.
2. Timezone Conversion: NBA times are US Eastern Time. Convert to local timezone (+12/13 hours for Beijing).
3. Live Data Delay: Live API has 1-3 second delay.
4. Update Frequency: Stats API updates slowly. Check once per minute max.

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More Information

See API_REFERENCE.md for complete endpoint documentation.