reconcile-yesterday

Compare actual NBA player participation against predicted sets across pipeline phases.

1|Updated May 24, 2025

Search Tags:#pipeline #NBA #reconciliation #data-analytics #backfill #predictions #boxscores

Ready to run

npx skills add https://github.com/najicham/nba-stats-scraper --skill reconcile-yesterday

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reconcile-yesterday
Source: https://github.com/najicham/nba-stats-scraper/tree/main/.claude/skills/reconcile-yesterday
Command: npx skills add https://github.com/najicham/nba-stats-scraper --skill reconcile-yesterday

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps operations teams identify and close gaps in yesterday's NBA prediction pipeline by comparing actual player participation against cached/featured/predicted datasets and orchestrating targeted backfills.

Core Features & Use Cases

Day-after reconciliation: Verifies boxscores arrival, player coverage, feature-store presence, and predictions for yesterday's games.
Gap identification: Highlights players who played but were not predicted, including HIGH_IMPACT, MEDIUM, and LOW categories.
Remediation orchestration: Suggests or triggers backfills across pipeline phases to restore data completeness.

Quick Start

Trigger the reconciliation for yesterday's date and review any gaps between played players and their predictions. For example, run: reconcile-yesterday --date "$RECON_DATE"

Frequently Asked Questions about reconcile-yesterday

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

FAQPage Schema

How do I reconcile yesterday's NBA prediction pipeline for missing boxscores and player coverage?▼

NBA prediction pipeline reconciliation compares actual player participation against cached, featured, and predicted sets to identify mismatches and coverage gaps across all six pipeline phases. It provides actionable remediation steps and backfill orchestration guidance based on the findings.

What is the best way to backfill pipeline gaps for NBA predictions from the previous day?▼

Backfilling pipeline gaps for NBA predictions involves running a day-after reconciliation that highlights players who played but were not predicted, categorized by HIGH_IMPACT, MEDIUM, and LOW priority. Remediation orchestration then suggests or triggers targeted backfills across pipeline phases to restore data completeness.

Can I run a data reconciliation check for a specific date instead of defaulting to yesterday?▼

Date-specific reconciliation is supported through an override option for a specific date, while the analysis defaults to yesterday's date. You can trigger the reconciliation for a custom date by running the command with the desired date parameter.

Why does my NBA data pipeline show mismatches between played players and their predictions?▼

NBA data pipeline mismatches between played players and predictions occur when actual player participation does not align with cached, featured, or predicted datasets. The reconciliation process identifies these gaps across all six pipeline phases and provides actionable remediation steps to resolve them.

Does NBA prediction reconciliation require any external dependencies or environment setup?▼

NBA prediction reconciliation requires no external dependencies or specific environment setup to operate. The skill independently identifies and reports gaps by comparing actual player participation to cached datasets and orchestrating targeted backfills across the pipeline phases.

What limitations exist when identifying HIGH_IMPACT player gaps in an NBA prediction pipeline?▼

Identifying HIGH_IMPACT player gaps in an NBA prediction pipeline is limited to yesterday's date by default, with an override option for a specific date. The analysis covers all six pipeline phases but focuses on comparing actual player participation against cached, featured, and predicted sets.