Audits Ludi-Bot Python code for 11 targeted failure and data quality issues.
npx skills add https://github.com/LudiInformatio/Ludi-Bot --skill ludi-audit
Please help me install this Agent Skill. Skill: ludi-audit Source: https://github.com/LudiInformatio/Ludi-Bot/tree/main/.gemini/skills/ludi-audit Command: npx skills add https://github.com/LudiInformatio/Ludi-Bot --skill ludi-audit
This Skill proactively identifies and flags Ludi-Bot-specific failure patterns, silent bugs, and technical debt that generic code reviews would miss, ensuring pipeline stability and data integrity.
bet_recommendations table, run /ludi-audit review [file(s)] to ensure schema consistency and prevent silent data corruption.Run the ludi-audit skill on the file utils/mappings.py to check for Ludi-specific gotchas.
High-intent search queries and answers about installing and using this skill.
To audit Python code for silent exception swallowing and database connection issues, you can run an 11-point audit that validates database connection practices within simulation loops and flags silent exception handling.
A schema synchronization check for bet_recommendations tables involves validating schema consistency before merging code changes to ensure pipeline stability and prevent silent data corruption.
Checking Python 3.11 f-string compliance and avoiding hardcoded roster data requires an automated audit that detects specific failure patterns and flags technical debt in your Python codebase.
You need to run a code review for BDL abbreviation normalization and canonical data joins when preparing to merge changes that affect data quality, ensuring canonical team IDs and composite IDs are handled correctly.
Yes, you can use a targeted code audit to detect technical debt and pipeline breakers before merging by scanning for specific failure patterns like improper player name resolution and inefficient API endpoint selection.
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