artifact-explorer

Identify, count and validate sports data artifacts across raw, derived and packages directories.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill eliminates the uncertainty around what sports data has been successfully captured and processed, providing immediate visibility into your data pipeline status.

Core Features & Use Cases

  • Artifact Discovery: Scan and count all captured games, players, and files across multiple sports.
  • Pipeline Gap Detection: Identify missing data between capture, derivation, and packaging stages.
  • Use Case: After running overnight data captures, use this Skill to quickly verify which sports have new data and identify any processing gaps that need attention.

Quick Start

Use the artifact-explorer skill to check what data we have in the artifacts directory.

Frequently Asked Questions about artifact-explorer

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

FAQPage Schema
How do I verify sports data capture success across my pipeline?

Data validation scans your artifacts directory to count and identify all captured games, players, and files across sports, revealing which data successfully moved through raw, derived, and package stages. Run the artifact-explorer skill after overnight captures to instantly see what's available and spot processing gaps.

Can I check artifact availability by sport and date?

Yes, artifact exploration provides per-sport and per-date visibility into captured data. The skill counts artifacts across all sports in your directory, handles ISO 8601 dates, and reports status in a human-readable table so you know exactly what data exists when.

How do I identify missing data between capture and packaging stages?

Pipeline gap detection compares artifact counts across raw, derived, and packaged data layers. The skill cross-checks directory scans with database counts to flag where data stops moving through your processing pipeline, pinpointing which stage needs attention.

What's the fastest way to scan a large sports data directory?

Read-only directory scanning with Rich-based tabular reporting delivers instant status without modifying files. The skill efficiently processes JSON indexes and counts artifacts across multiple sports in a single pass, producing immediate visibility into your full dataset.

Can I use this skill without database access?

The skill performs standalone directory scanning and JSON index parsing to report what artifacts exist. Cross-checking with database counts is optional; you can use artifact-explorer for directory-only validation if your database isn't available.