Audit dimension join accuracy and surface unmatched codes via psql.
npx skills add https://github.com/josca42/varro --skill audit-dim-joins
Please help me install this Agent Skill. Skill: audit-dim-joins Source: https://github.com/josca42/varro/tree/main/.claude/archive/audit-dim-joins Command: npx skills add https://github.com/josca42/varro --skill audit-dim-joins
Validate and annotate the joins between fact and dimension tables to ensure accurate aggregations, surface join gaps, and provide actionable notes for per-table and subject-level documentation.
Run the audit on a subject with uv run python scripts/audit_dim_joins.py audit-subject <subject_path> to generate match rates and notes.
High-intent search queries and answers about installing and using this skill.
To audit dimension joins in PostgreSQL, read your subject docs and run the audit CLI script against a fact table to calculate match rates and surface unmatched codes. The tool validates joins using psql and appends the audit results as per-table notes.
Dimension join gaps occur when fact table records lack matching dimension entries, leading to inaccurate aggregations. They affect data quality by dropping unmatched rows or misattributing metrics, requiring an audit workflow to detect match rates and annotate the gaps for remediation.
You validate fact table match rates using psql by running the provided Python audit script which executes psql commands to compare fact and dimension tables. The script calculates match rates, identifies unmatched codes, and outputs the results for your subject notes.
Yes, you can run an audit across an entire subject by executing the script with the audit-subject parameter and passing the subject path. This generates overall match rates and summary notes for all fact tables within that subject directory.
The best way to document dimension join issues is to append practical audit notes directly to per-table docs and summarize them at the subject level. This workflow ensures data ops teams can iteratively update dimension links and notes based on detected join gaps.
Inaccurate aggregations without SQL errors often stem from silent dimension join gaps where fact rows have unmatched dimension codes. Running an audit script to determine match rates and identify unmatched codes will surface these hidden hierarchy level issues.
mark64oswaldDesign star and snowflake schemas with fact and dimension tables.
krishnakanthb13Audit codebases for OWASP Top 10 vulnerabilities and generate SECURITY.md reports.
TeamDay-AIQuery and manage PostgreSQL databases using the psql CLI.
jonathanmalkinAnalyze web app funnel metrics, identify bottlenecks, and design RICE-scored experiments.
