nba-data

machina-sports/sports-skills · updated Apr 8, 2026

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$npx skills add https://github.com/machina-sports/sports-skills --skill nba-data
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summary

Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.

skill.md

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_teams to 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:

  1. 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:

  1. Derive season year from currentDate
  2. Call get_standings(season=<derived_year>)
  3. 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:

  1. 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:

  1. Call get_scoreboard(date="<yesterday>") to find the event_id
  2. 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:

  1. Call get_injuries()
  2. 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:

  1. Derive season year from currentDate
  2. 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 / get_betting_odds — not available. For prediction market odds, use the polymarket or kalshi skill.
  • search_teams — does not exist. Use get_teams instead.
  • get_box_score — does not exist. Use get_game_summary instead.
  • get_player_ratings — does not exist. Use get_player_stats instead.

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:

  1. Catch silently and try alternatives
  2. If team name given instead of ID, use get_teams to find the ID first
  3. 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

How to use nba-data on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/machina-sports/sports-skills --skill nba-data

The skills CLI fetches nba-data from GitHub repository machina-sports/sports-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/nba-data

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

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.871 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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