Skills engineering-nba-data
📦

engineering-nba-data

Content revision r1 Low Risk ⚙️ External commands🌐 Network access📁 Filesystem access

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.

Supports: Claude Codex Code(CC)
📊 75 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "engineering-nba-data" from https://skillstore.io/skills/emz1998-engineering-nba-data.md and its manifest at https://skillstore.io/api/skills/emz1998-engineering-nba-data/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/emz1998-engineering-nba-data.md Signed manifest GET /api/skills/emz1998-engineering-nba-data/manifest Signed lockfile GET /api/skills/emz1998-engineering-nba-data/lockfile

Test it

Using "engineering-nba-data". Find LeBron James career statistics and prepare them for a notebook analysis.

Expected outcome:

A step-by-step plan identifying the player lookup, the career statistics endpoint, required parameters, and recommended DataFrame validation checks.

Using "engineering-nba-data". Create a daily team standings report.

Expected outcome:

  • Recommended standings and schedule endpoints.
  • Parameter guidance for season and league values.
  • A concise export plan for CSV or database storage.

Using "engineering-nba-data". Explain why my NBA endpoint request returns no rows.

Expected outcome:

A troubleshooting checklist covering player IDs, season format, league ID, endpoint availability, headers, proxy settings, and timeout handling.

Security Audit

Low Risk
v6 • 7/21/2026 Open versioned report

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.

298
Files scanned
71,914
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (3)

Low
docs/nba_api/stats/endpoints/responses/commonallplayers.json:1
Unscanned file (too_large) — manual review required
[unscanned: too_large]
The file was not statically scanned because it is oversized. Its contents remain unreviewed, so this finding must be retained for manual review.
Low
docs/nba_api/stats/endpoints/responses/dunkscoreleaders.json:1
Unscanned file (too_large) — manual review required
[unscanned: too_large]
The file was not statically scanned because it is oversized. Its contents remain unreviewed, so this finding must be retained for manual review.
Low
docs/nba_api/stats/endpoints/responses/scheduleleaguev2.json:1
Unscanned file (too_large) — manual review required
[unscanned: too_large]
The file was not statically scanned because it is oversized. Its contents remain unreviewed, so this finding must be retained for manual review.

Risk Factors

⚙️ External commands (50)
docs/examples/Basics.ipynb:9 docs/examples/Basics.ipynb:285 docs/examples/Basics.ipynb:286 docs/examples/Basics.ipynb:287 docs/examples/Basics.ipynb:291 docs/examples/Basics.ipynb:304 docs/examples/Finding Games.ipynb:9 docs/examples/Finding Games.ipynb:12 docs/examples/Finding Games.ipynb:825 docs/examples/Finding Games.ipynb:1100 docs/examples/Finding Games.ipynb:1105 docs/examples/LiveData.ipynb:8 docs/examples/LiveData.ipynb:2692 docs/examples/PlayByPlay.ipynb:13 docs/examples/PlayByPlay.ipynb:14 docs/examples/PlayByPlay.ipynb:16 docs/examples/PlayByPlay.ipynb:297 docs/examples/PlayByPlay.ipynb:298 docs/examples/PlayByPlay.ipynb:299 docs/examples/PlayByPlay.ipynb:300 docs/examples/PlayByPlay.ipynb:301 docs/examples/PlayByPlay.ipynb:303 docs/examples/PlayByPlay.ipynb:330 docs/examples/PlayByPlay.ipynb:355 docs/examples/PlayByPlay.ipynb:522 docs/examples/PlayByPlay.ipynb:524 docs/examples/PlayByPlay.ipynb:526 docs/examples/PlayByPlay.ipynb:552 docs/examples/PlayByPlay.ipynb:556 docs/examples/PlayByPlay.ipynb:565 docs/examples/PlayByPlay.ipynb:566 docs/examples/PlayByPlay.ipynb:568 docs/examples/PlayByPlay.ipynb:652 docs/examples/PlayByPlay.ipynb:653 docs/examples/PlayByPlay.ipynb:655 docs/examples/PlayByPlay.ipynb:742 docs/nba_api/stats/endpoints/playerindex.md:28-32 SKILL.md:14 SKILL.md:16 SKILL.md:17 SKILL.md:18 SKILL.md:24 SKILL.md:25 SKILL.md:26 SKILL.md:32 SKILL.md:33 SKILL.md:35 SKILL.md:36 SKILL.md:37 SKILL.md:41
🌐 Network access (50)
docs/examples/Basics.ipynb:11 docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb:58 docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb:18 docs/examples/LiveData.ipynb:2756 docs/examples/LiveData.ipynb:2778 docs/examples/LiveData.ipynb:2800 docs/examples/LiveData.ipynb:2822 docs/examples/LiveData.ipynb:2844 docs/examples/LiveData.ipynb:2866 docs/examples/LiveData.ipynb:2888 docs/examples/LiveData.ipynb:2921 docs/examples/LiveData.ipynb:2945 docs/examples/LiveData.ipynb:2971 docs/examples/LiveData.ipynb:2997 docs/examples/LiveData.ipynb:3021 docs/examples/LiveData.ipynb:3047 docs/examples/LiveData.ipynb:3073 docs/examples/LiveData.ipynb:3099 docs/examples/LiveData.ipynb:3137 docs/examples/LiveData.ipynb:3159 docs/examples/LiveData.ipynb:3181 docs/examples/LiveData.ipynb:3203 docs/examples/LiveData.ipynb:3225 docs/examples/LiveData.ipynb:3247 docs/examples/LiveData.ipynb:3280 docs/examples/LiveData.ipynb:3304 docs/examples/LiveData.ipynb:3330 docs/examples/LiveData.ipynb:3356 docs/examples/LiveData.ipynb:3382 docs/examples/LiveData.ipynb:3408 docs/examples/LiveData.ipynb:3434 docs/examples/LiveData.ipynb:3460 docs/examples/LiveData.ipynb:3498 docs/examples/LiveData.ipynb:3520 docs/examples/LiveData.ipynb:3542 docs/examples/LiveData.ipynb:3564 docs/examples/LiveData.ipynb:3586 docs/examples/LiveData.ipynb:3608 docs/examples/LiveData.ipynb:3630 docs/examples/LiveData.ipynb:3663 docs/examples/LiveData.ipynb:3687 docs/examples/LiveData.ipynb:3713 docs/examples/LiveData.ipynb:3739 docs/examples/LiveData.ipynb:3765 docs/examples/LiveData.ipynb:3791 docs/examples/LiveData.ipynb:3817 docs/examples/LiveData.ipynb:3843 docs/examples/PlayByPlay.ipynb:558 docs/nba_api/library/http.md:50 docs/nba_api/library/http.md:54
📁 Filesystem access (2)
docs/examples/LiveData.ipynb:7 docs/examples/PlayByPlay.ipynb:570
Audited by: claude View Audit History →
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Academic citations (APA · BibTeX · CFF)

APA citation

Emz1998. (2026). engineering-nba-data security audit report (audit version 6) [Author version unspecified]. Skillstore. https://skillstore.io/skills/emz1998-engineering-nba-data/audits/6

BibTeX citation

@techreport{emz1998-emz1998-engineering-nba-data-2026, author = {Emz1998}, title = {engineering-nba-data security audit report (audit version 6)}, institution = {Skillstore}, year = {2026}, number = {6}, url = {https://skillstore.io/skills/emz1998-engineering-nba-data/audits/6}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "engineering-nba-data security audit report (audit version 6)" version: "unspecified" type: report authors: - name: "Emz1998" date-released: "2026-07-21" url: "https://skillstore.io/skills/emz1998-engineering-nba-data/audits/6" identifiers: - type: other value: "skillstore:emz1998-engineering-nba-data:audit:6" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium
55
Architecture
85
Maintainability
87
Content
67
Community
91
Spec Compliance

What You Can Build

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.

Try These Prompts

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.

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.

Avoid

  • 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.

Frequently Asked Questions

What Python package does this skill focus on?
It focuses on the nba_api package for NBA stats, live data, and static lookup workflows.
Can it help with pandas DataFrames?
Yes. It can guide use of endpoint response methods that return pandas DataFrames when supported.
Does it include live game data guidance?
Yes. It includes references for live endpoints such as box scores, play-by-play data, and scoreboards.
Can it solve endpoint errors automatically?
No. It can suggest checks for parameters, headers, proxies, timeouts, and endpoint changes.
Does it require internet access?
Most endpoint workflows require network access to NBA services, while static lookup examples may work locally after installation.
Is it only for analysts?
No. Analysts, data engineers, students, and developers can use it for NBA data extraction and transformation tasks.

Developer Details

Author

Emz1998

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/Emz1998/avaris-ai/tree/master/.claude/skills/nba-data

Ref

1ffa7643651792ccb4bd3b15d924d2c97edff755

Maintenance freshness

7/22/2026

Usage

2 downloads · 1 views

File structure

📁 docs/

📁 examples/

📁 Home Team Win-Loss Modeling/

📄 Home Team Win-Loss Data Prep.ipynb

📄 Home Team Win-Loss Modeling.ipynb

📄 Basics.ipynb

📄 Finding Games.ipynb

📄 LiveData.ipynb

📄 PlayByPlay.ipynb

📁 nba_api/

📁 library/

📄 http.md

📁 live/

📁 endpoints/

📁 stats/

📁 endpoints/

📁 endpoints_output/

📁 library/

📁 static/

📄 endpoints_data_structure.md

📄 examples.md

📁 tools/

📁 stats/

📄 endpoint_analysis_format.md

📄 package_structure.md

📄 scripts.md

📄 table_of_contents.md

📄 skill-report.schema.json

📄 SKILL.md