# Analyze NBA Data with nba\_api

NBA data work often requires finding the right endpoint, parameters, and response format. This skill guides Claude, Codex, and Claude Code through nba_api extraction, transformation, and analysis workflows.

## Install

```bash
npx skillstore add emz1998/engineering-nba-data
```

## Metadata

- Status: approved
- Slug: emz1998-engineering-nba-data
- Skillstore revision: r1
- Version status: missing
- Tree hash: cdce9a1c9e5202bef725fc9b0c3f6032845a2722ffafc92d2ff867638cd4b378
- Author: Emz1998
- GitHub username: Emz1998
- License: MIT
- Repository: https://github.com/Emz1998/avaris-ai/tree/master/.claude/skills/nba-data
- Ref: 1ffa7643651792ccb4bd3b15d924d2c97edff755
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: confirmation\_required
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network, filesystem
- Quality score: 75
- Public page: https://skillstore.pages.dev/skills/emz1998-engineering-nba-data
- Manifest: https://skillstore.pages.dev/api/skills/emz1998-engineering-nba-data/manifest

## Capabilities

- Find appropriate nba\_api endpoints for player, team, game, standings, and live data tasks.
- Explain required parameters such as season, league ID, team ID, player ID, and game ID.
- Guide conversion of endpoint responses into dictionaries, JSON strings, or pandas DataFrames.
- Support static lookup workflows for NBA players and teams.
- Help design data pipelines that export NBA results to CSV, JSON, or database tables.
- Reference included endpoint documentation and examples for available fields and datasets.

## Use Cases

- Build a player statistics dataset: Collect career, season, or game-level player statistics and prepare them for analysis.
- Create team performance reports: Retrieve team schedules, standings, box scores, and league dashboards for recurring reporting.
- Prototype NBA data pipelines: Use endpoint guidance to transform NBA API responses into files or database-ready tables.

## Prompt Templates

### Find a player ID

```
Use the NBA data skill to find the nba_api workflow for looking up a player ID by full name.
```

### Fetch season game logs

```
Use nba_api to retrieve game logs for a named NBA player in a specific season and return the fields I should validate.
```

### Compare team box scores

```
Help me choose nba_api endpoints to compare two teams across recent games, then outline a pandas transformation plan.
```

### Design a repeatable pipeline

```
Design a reliable NBA data pipeline that pulls standings, schedules, and player stats, handles endpoint limits, and exports clean tables.
```

## Limitations

- Requires the nba\_api package and working access to NBA data endpoints.
- Does not guarantee endpoint availability because NBA services can change or throttle requests.
- Does not provide licensed commercial NBA data beyond what the configured API returns.
- Large historical pulls may need rate limiting, caching, and manual validation.

## Best Practices

- Validate IDs with static player and team lookups before calling endpoint classes.
- Cache repeated endpoint responses and respect service limits during large data pulls.
- Check response dataset names and columns before building downstream transformations.

## Anti Patterns

- Do not assume every endpoint returns a single DataFrame or the same column layout.
- Do not run large historical pulls without retry, timeout, and caching plans.
- Do not hardcode player or team IDs without documenting how they were resolved.

## Security Audit

- Audited at: 2026-07-21T07:28:32.173\+00:00
- Summary: Most static matches are false positives caused by Markdown backticks, serialized notebooks, NBA endpoint documentation, and example data. Three oversized JSON response files were not scanned and remain confirmed low-severity review gaps. No prompt injection, host reconnaissance, command execution, path traversal, or credential-exfiltration intent was evidenced in the reviewed findings. Static review was capped at 400/564 representative findings; omitted static matches are unconfirmed, so automatic publishing stays disabled until manual review.

## Stats

- Views: 1
- Downloads: 4
- Favorites: 0
- Popularity score: 0