Artifact Explorer Skill

Scan artifact directories and report data availability across sports and dates.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/fstr21/sportsbot --skill artifact-explorer-skill
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Artifact Explorer Skill
Source: https://github.com/fstr21/sportsbot/tree/main/.claude/skills/artifact-explorer
Command: npx skills add https://github.com/fstr21/sportsbot --skill artifact-explorer-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rich, and includes scripts (resource) components.

What problem does it solve?

Users often struggle to understand what data has been captured, if pipelines are working correctly, or if data is missing between processing stages. This Skill provides clear, immediate visibility into the data artifact lifecycle, eliminating guesswork.

Core Features & Use Cases

  • Scan & Summarize: Automatically scans artifact directories, counting games, players, and files for each sport and date.
  • Pipeline Stage Analysis: Identifies which pipeline stages (raw, derived, packages) have data and highlights any gaps, such as data captured but not yet derived.
  • Integrity Checks: Validates artifact integrity (e.g., corrupt JSON) and compares artifact counts against the database.
  • Use Case: After a capture script runs, use this Skill to quickly verify that artifacts were created, check for missing data, or debug why data isn't showing up in the database.

Quick Start

Explore all artifacts

python .claude/skills/artifact-explorer/scripts/explore_artifacts.py

Check NBA artifacts for a specific date

python .claude/skills/artifact-explorer/scripts/explore_artifacts.py --sport nba --date 2025-10-22

Frequently Asked Questions about Artifact Explorer Skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify that data artifacts were successfully captured after a pipeline runs?

Artifact verification scans your artifact directories to count games, players, and files per sport and date, confirming capture success. Run the explore script with optional sport and date filters to see exactly what was created and identify any missing data between raw, derived, and packaged stages.

What's the best way to debug missing data in sports pipeline stages?

Pipeline stage analysis identifies gaps by checking which processing stages (raw, derived, packages) contain data for each sport and date. The Skill performs directory scans, reads index.json files, and presents results in formatted tables so you can pinpoint exactly where data is missing.

Can I check artifact integrity and compare file counts against the database?

Yes. The Skill validates artifact integrity by checking for corrupt JSON, counts files across sports and dates, and compares artifact counts against your database to surface discrepancies and ensure data consistency across pipeline stages.

How do I quickly see which sports and dates have available artifacts?

Run the explore script to automatically scan artifact directories and generate a Rich-formatted summary showing games and files per sport and date. The script handles missing directories gracefully and supports filtering by sport or specific date for focused investigation.

What happens if artifact directories are missing or incomplete?

The Skill handles missing directories gracefully without failing. It scans available artifact paths, counts what exists, and clearly highlights gaps in data capture, so you can identify which sports, dates, or pipeline stages are incomplete.