nba-data▌
machina-sports/sports-skills · updated Apr 8, 2026
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Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.
NBA Data
Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.
Setup
Before first use, check if the CLI is available:
which sports-skills || pip install sports-skills
If pip install fails with a Python version error, the package requires Python 3.10+. Find a compatible Python:
python3 --version # check version
# If < 3.10, try: python3.12 -m pip install sports-skills
# On macOS with Homebrew: /opt/homebrew/bin/python3.12 -m pip install sports-skills
No API keys required.
Quick Start
Prefer the CLI — it avoids Python import path issues:
sports-skills nba get_scoreboard
sports-skills nba get_standings --season=2025
sports-skills nba get_teams
CRITICAL: Before Any Query
CRITICAL: Before calling any data endpoint, verify:
- Season year is derived from the system prompt's
currentDate— never hardcoded. - If only a team name is provided, call
get_teamsto resolve the team ID before using team-specific commands.
Choosing the Season
Derive the current year from the system prompt's date (e.g., currentDate: 2026-02-18 → current year is 2026).
- If the user specifies a season, use it as-is.
- If the user says "current", "this season", or doesn't specify: The NBA season runs October–June. If the current month is October–December, the active season year matches the current year. If January–June, the active season started the previous calendar year (use that year as the season).
Commands
| Command | Description |
|---|---|
get_scoreboard |
Live/recent NBA scores |
get_standings |
Standings by conference |
get_teams |
All 30 NBA teams |
get_team_roster |
Full roster for a team |
get_team_schedule |
Schedule for a specific team |
get_game_summary |
Detailed box score and scoring plays |
get_leaders |
NBA statistical leaders |
get_news |
NBA news articles |
get_play_by_play |
Full play-by-play for a game |
get_win_probability |
Win probability chart data |
get_schedule |
Schedule for a specific date or season |
get_injuries |
Injury reports across all teams |
get_transactions |
Recent transactions |
get_futures |
Futures/odds markets |
get_depth_chart |
Depth chart for a team |
get_team_stats |
Team statistical profile |
get_player_stats |
Player statistical profile |
See references/api-reference.md for full parameter lists and return shapes.
Examples
Example 1: Today's scores User says: "What are today's NBA scores?" Actions:
- Call
get_scoreboard()Result: All live and recent NBA games with scores and status
Example 2: Conference standings User says: "Show me the Western Conference standings" Actions:
- Derive season year from
currentDate - Call
get_standings(season=<derived_year>) - Filter results for Western Conference Result: Western Conference standings table with W-L, PCT, GB per team
Example 3: Team roster User says: "Who's on the Lakers roster?" Actions:
- Call
get_team_roster(team_id="13")Result: Full Lakers roster with name, position, jersey number, height, weight
Example 4: Game box score User says: "Show me the full box score for last night's Celtics game" Actions:
- Call
get_scoreboard(date="<yesterday>")to find the event_id - Call
get_game_summary(event_id=<id>)for full box score Result: Complete box score with per-player stats and scoring plays
Example 5: Injury report User says: "Who's injured on the Lakers?" Actions:
- Call
get_injuries() - Filter results for Los Angeles Lakers (team_id=13) Result: Lakers injury list with player name, position, status, and injury type
Example 6: Player statistics User says: "Show me LeBron's stats this season" Actions:
- Derive season year from
currentDate - Call
get_player_stats(player_id="1966", season_year=<derived_year>)Result: Season stats by category with value, rank, and per-game averages
Commands that DO NOT exist — never call these
/get_odds— not available. For prediction market odds, use the polymarket or kalshi skill.get_betting_odds— does not exist. Usesearch_teamsget_teamsinstead.— does not exist. Useget_box_scoreget_game_summaryinstead.— does not exist. Useget_player_ratingsget_player_statsinstead.
If a command is not listed in the Commands table above, it does not exist.
Error Handling
When a command fails, do not surface raw errors to the user. Instead:
- Catch silently and try alternatives
- If team name given instead of ID, use
get_teamsto find the ID first - Only report failure with a clean message after exhausting alternatives
Troubleshooting
Error: sports-skills command not found
Cause: Package not installed
Solution: Run pip install sports-skills
Error: Team not found by ID
Cause: Wrong or outdated ESPN team ID used
Solution: Call get_teams to get the current list of all 30 NBA teams with their IDs
Error: No data returned for a future game
Cause: ESPN only returns data for completed or in-progress games
Solution: Use get_schedule to see upcoming game details; get_scoreboard only covers active/recent games
Error: Offseason — scoreboard returns 0 events
Cause: No games scheduled during the offseason (July–September)
Solution: Use get_standings or get_news instead; use get_schedule to find when the season resumes
How to use nba-data on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add nba-data
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches nba-data from GitHub repository machina-sports/sports-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate nba-data. Access the skill through slash commands (e.g., /nba-data) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★71 reviews- ★★★★★Fatima Khan· Dec 28, 2024
I recommend nba-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Luis Ramirez· Dec 24, 2024
Keeps context tight: nba-data is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Arya Bhatia· Dec 20, 2024
Useful defaults in nba-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Luis Diallo· Dec 16, 2024
Keeps context tight: nba-data is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ira Ramirez· Dec 16, 2024
nba-data has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Dec 4, 2024
Registry listing for nba-data matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arya Chawla· Nov 27, 2024
We added nba-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Nov 23, 2024
nba-data reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Arya Agarwal· Nov 23, 2024
We added nba-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Luis Bhatia· Nov 19, 2024
nba-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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